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Full text of "International Journal of Computer Science and Information Security IJCSIS June 2010"

UCSIS Vol. 8 No. 3, June 2010 
ISSN 1947-5500 



International Journal of 
Computer Science 
& Information Security 



© IJCSIS PUBLICATION 2010 



Editorial 
Message from Managing Editor 

The International Journal of Computer Science and Information Security is an 
English language periodical on research in general computer science and information 
security which offers prompt publication of important technical research work, 
whether theoretical, applicable, or related to implementation. 

Target Audience: IT academics and business people concerned with computer science 
and security; university IT faculties; industry IT departments; government departments; 
the financial industry; the mobile industry and the computing industry. 

Coverage includes: security infrastructures, network security: Internet security, 
content protection, cryptography, steganography and formal methods in information 
security; multimedia, image processing, software, information systems, intelligent 
systems, web services, wireless communication, networking and technologies. 

Thanks to authors who contributed papers to the June 2010 issue and the reviewers, 
for providing valuable feedback comments. IJCSIS June 2010 Issue (Vol. 8, No. 3) has 
an acceptance rate of 30 %. 

Available at http:/ / sites.qooqle.com/ site/ iicsis/ 

IJCSIS Vol. 8, No. 3, June 2010 Edition 
ISSN 1947-5500 © IJCSIS 2010, USA. 

Abstracts Indexed by (among others): 
LiOUQie scholar = r\i**^&r* *>wllUa 



b&rcMinch engin* tor sclc-ree 



IJCSIS EDITORIAL BOARD 



Dr. Gregorio Martinez Perez 

Associate Professor - Professor Titular de Universidad, University of Murcia 
(UMU), Spain 

Dr. M. Emre Celebi, 

Assistant Professor, Department of Computer Science, Louisiana State University 
in Shreveport, USA 

Dr. Yong Li 

School of Electronic and Information Engineering, Beijing J iaotong University, 
P. R. China 

Prof. Hamid Reza Naji 

Department of Computer Enigneering, Shahid Beheshti University, Tehran, Iran 

Dr. Sanjayjasola 

Professor and Dean, School of Information and Communication Technology, 
Gautam Buddha University 

Dr Riktesh Srivastava 

Assistant Professor, Information Systems, Skyline University College, University 
City of Sharjah, Sharjah, PO 1797, UAE 

Dr. Siddhivinayak Kulkarni 

University of Ballarat, Ballarat, Victoria, Australia 

Professor (Dr) Mokhtar Beldjehem 

Sainte-Anne University, Halifax, NS, Canada 

Dr. Alex Pappachen J ames, (Research Fellow) 

Queensland Micro-nanotechnology center, Griffith University, Australia 



TABLE OF CONTENTS 



1. Paper 27051043: Implementation of SPIN Model Checker for Formal Verification of Distance 
Vector Routing Protocol (pp. 1-6) 

Kashif Javed, Department of Information Technologies, Abo Akademi University, Joukahaisenkatu, Turku, 

FIN-20520, Finland 

Asifa Kashif, Department of Electrical Engineering, National University- Foundation for Advancement of 

Science and Technology, A.K. Brohi Road, H-ll/4, Islamabad, 44000, Pakistan 

Elena Troubitsyna, Department of Information Technologies, Abo Akademi University, Turku, FIN-20520, 

Finland 

2. Paper 31051062: Integrated Queuing based Energy-Aware Computing in MANET (pp. 7-10) 

Dr. P. K. Suri, Dean and Professor, Faculty of Science, Deptt. ofComp. Sci. &Appl. Kurukshetra 
University, Kurukshetra, Haryana, India 

Kavita Taneja, Assistant Professor, M. M. Inst, of Computer Tech. & Business Mgmt. Maharishi 
Markandeshwar University, Mullana, Haryana, India 

3. Paper 30051057: A Review of Negotiation Agents in e-commerce (pp. 11-20) 

Sahar Ebadi, Department of Information System, Faculty of Computer Science and Information 

Technology, University Putra Malaysia, Serdang, Malaysia 

Md. Nasir Sulaiman, Department of Information System, Faculty of Computer Science and Information 

Technology, University Putra Malaysia, Serdang, Malaysia 

Masrah Azrifah Azmi Murad, Department of Information System, Faculty of Computer Science and 

Information Technology, University Putra Malaysia, Serdang, Malaysia 

4. Paper 31051071: Customized Digital Road Map Building using Floating Car GPS Data (pp. 21-29) 

G. Rajendran, Assistant Professor of Computer Science, Thiruvalluvar Government Arts College, 

Rasipuram-637401 , Tamilnadu, India 

Dr. M. Arthanari, Director, Bharathidasan School of Computer Applications, Ellispettai-6381 16, 

Tamilnadu, India 

M. Sivakumar, Doctoral Research Scholar, Anna University, Coimbatore, Tamilnadu, India 

5. Paper 31051077: Robust stability check of fractional control law applied to a LEO (Low Earth 
Orbit) Satellite (pp. 30-36) 

Ouadid EL Figuigui, Noureddine Elalami, 

Laboratoire dAutomatique et Informatique Industrielle EMI, Morocco 

6. Paper 30051054: Performance Evaluation of Genetic Algorithm For Solving Routing Problem In 
Communication Networks (pp. 37-43) 

Ehab Rushdy Mohamed, Faculty of Computer and Informatics, Zagazig University, Zagazig, Egypt, 

Mahmoud Ibrahim Abdalla, Faculty of Engineering, Zagazig University, Zagazig, Egypt, 

Ibrahim Elsayed Zidan, Faculty of Engineering, Zagazig University, Zagazig, Egypt, 

Ibrahim Mahmoud El-Henawy, Faculty of Computer and Informatics, Zagazig University, Zagazig, Egypt 

7. Paper 25051033: Testing Equivalence of Regular Expressions (pp. 44-46) 

Keehang Kwon, Department of Computer Engineering, Dong- A University, Busan, Republic of Korea 
Hong Pyo Ha, Department of Computer Engineering, Dong-A University, Busan, 
Republic of Korea 

8. Paper 31051079: CRS, a Novel Ensemble Construction Methodology (pp. 47-51) 

Navid Kardan, Computer Engineering Dep. IUST, Tehran, Iran 
Morteza Analoui, Computer Engineering Dep., IUST, Tehran, Iran 



9. Paper 31051072: Routing Optimization Technique Using M/M/l Queuing Model & Genetic 
Algorithm (pp. 52-58) 

Madiha Sarfraz, M. Younus Javed, Muhammad Almas Anjum, Shaleeza Sohail 

Department of Computer Engineering, College of Electrical & Mechanical Engineering, Pakistan 

10. Paper 31051083: Architectural Description of an Automated System for Uncertainty Issues 
Management in Information Security (pp. 59-67) 

Haider Abbas, Department of Electronic Systems, Royal Institute of Technology, Sweden 
Christer Magnus son, Department of Computer and System Sciences, Stockholm University, Sweden 
Louise Yngstrom, Department of Computer and System Sciences, Stockholm University, Sweden 
Ahmed Hemani, Department of Electronic Systems, Royal Institute of Technology, Sweden 

11. Paper 14041018: Driving Architectural Design through Business Goals (pp. 68-71) 

Lena Khaled, Software Engineering Department, Zarqa Private University, Amman, Jordon 

12. Paper 11051005: Distributed Information Sharing Cooperation In Dynamic Channel Allocation 
Scheme (pp. 72-79) 

Mr. P. Jesu Jayarin, Sathyabama University, Chennai-119, India. 
Dr. T. Ravi, KCG college of Technology, Chennai-97, India. 

13. Paper 15051008: Key Generation For AES Using Bio-Metic Finger Print For Network Data 
Security (pp. 80-85) 

Dr. R. Seshadri, Director, University Computer Center, Sri Venkateswara University, Tirupati, 

T. Raghu Trivedi, Research Scholar, Department of Computer Science, Sri Venkateswara University, 

Tirupati. 

14. Paper 18051018: Classification of Five Mental Tasks Based on Two Methods of Neural Network 
(pp. 86-92) 

Vijay Khare, Jaypee Institute of Information Technology, Dept. of Electronics and Communication, 
Engineering, Nioda, India. 

Jayashree Santhosh, Indian Institute of Technology, Computer Services Centre, Delhi, India. 
SnehAnand, Indian Institute of Technology, Centre for Biomedical Engineering Centre, Delhi, India. 
Manvir Bhatia, Sir Ganga Ram Hospital, Department of Sleep Medicine, New Delhi, India 

15. Paper 25051047: Sixth order Butterworth Characteristics using LV MOCCII and Grounded 
Components (pp. 93-97) 

T. Parveen, Electronics Engineering Department, Z. H. College of Engineering & Technology, AMU, 
Aligarh, India 

16. Paper 27051042: A Lightweight Secure Trust-based Localization Scheme for Wireless Sensor 
Networks (pp. 98-104) 

P. Pandarinath, Associate Professor, CSE, Sir C R, Reddy College of Engineering, Eluru-534001, Andhra 

Pradesh 

M. Shashi, Head of the Department, Dept. OfCS&SE, Andhra University, Visakhapatnam- 530 003, 

Andhra Pradesh 

Allam Appa Rao, Vice Chancellor, JNTU Kakinada, Kakinada, Andhra Pradesh 

17. Paper 30051051: Mechanism to Prevent Disadvantageous Child Node Attachment in HiLOW (pp. 
105-110) 

Lingeswari V.Chandra, Kok-Soon Chai and Sureswaran Ramadass, National Advanced IPv6 Centre, 

Universiti Sains Malaysia 

Gopinath Rao Sinniah, MIMOS Berhad, 57000 Kuala Lumpur 



18. Paper 30051052: Rough Entropy as Global Criterion for Multiple DNA Sequence Alignment (pp. 
111-118) 

Sara El-Sayed El-Metwally, Demonstrator, Computer Science Departement, Faculty of Computer and 

information Science, Mansoura University, Egypt. 

Dr. ElSayed Foad Radwan, Lecturer, Computer Science Departement, Faculty of Computer and 

information Science, Mansoura University, Egypt. 

Ass. Prof. Taker Tawfek Hamza, Vice Dean for Graduate Studies and Research, Assistant Professor, 

Computer Science Departement, Faculty of Computer and information Science, Mansoura University, 

Egypt. 

19. Paper 30051053: Weighted Attribute Fusion Model for Face Recognition (pp. 119-125) 

S. Sakthivel, Assistant Professor, Department of Information Technology, Sona college of Technology, 
Salem, India 

Dr. R. Lakshmipathi, Professor, Department of Electrical and Electronic Engineering, St. Peter's 
Engineering College, Chennai, India 

20. Paper 30051055: A DNA and Amino Acids-Based Implementation of Playfair Cipher (pp. 126-133) 

Mona Sabry, Mohamed Hashem, Taymoor Nazmy, Mohamed Essam Khalifa 

Computer Science department, Faculty of Computer Science and information systems, Ain Shams 

University, Cairo, Egypt. 

21. Paper 30051061: Ultra Wideband Slot Antenna with Reconfigurable Notch bands (pp. 134-139) 

/. William and R. Nakkeeran, Department of Electronics and Communication Engineering 
Pondicherry Engineering College Puducherry, India . 605014. 

22. Paper 31051073: UWB Slot Antenna with Rejection of IEEE 802.11a Band (pp. 140-145) 

J. William and R. Nakkeeran 

Department of Electronics and Communication Engineering, Pondicherry Engineering College, 

Puducherry, India . 605014. 

23. Paper 31051086: A Study Of Various Load Balancing Techniques In Internet (pp. 146-153) 

M. Azath, Research Scholar, Anna University, Coimbatore. 

Dr. R.S.D. Wahida banu, Research Supervisor, Anna University, Coimbatore. 

24. Paper 30051058: Laboratory Study of Leakage Current and Measurement of ESDD of 
Equivalent Insulator Flat Model under Various Polluted Conditions (pp. 154-158) 

N. Narmadhai, Senior Lecturer, Dept of FEE Government College of Technology Coimbatore, India 

S. Suresh, PG Scholar, Dept of FEE, Government College of Technology, Coimbatore, India 

Dr. A. Ebenezer Jeyakumar, Director (Academics), SNR Sons Charitable Trust, SREC Coimbatore, India 

25. Paper 31051076: SSL/TLS Web Server Load Optimization using Adaptive SSL with Session 
Handling Mechanism (pp. 159-164) 

R. K. Pateriya, J. L. Rana, S. C Shrivastava 

Department of Computer Science & Engineering and Information Technology, Maulana Azad National 

Institute of Technology, Bhopal, India 

26. Paper 15051011: An Enhancement On Mobile TCP Socket (pp. 165-168) 

S. Saravanan, Research Scholar, Sathyabama University, Chennai-119, India. 

Dr. T. Ravi, Prof & Head, Dept of CSE ,KCG College of Technology , Chennai, India 



27. Paper 15051017: Modern Computer Graphics Technologies Used at Educational Programs and 
Some Graphical output screens (pp. 169-171) 

N. Suresh Kumar, S. Amarnadh, K. Srikanth, Ch. Heyma Raju, 

GIT, GIT AM University, Visakhapatnam 

D.V. Rama Koti Reddy, College of Engineering, Andhra University, Visakhapatnam 

R. Ajay Suresh Babu, Raghu Engineering College, Visakhapatnam 

K. Naga Soujanya, GIS, GITAM University, Visakhapatnam 

28. Paper 20051023: Impact of language morphologies on Search Engines Performance for Hindi and 
English language (pp. 172-178) 

Dr. S.K Dwivedi, Reader and Head, Computer Science Dept, BBAU, Lucknow, India. 
Rajesh Kr. Gautam, Research Scholar, Computer Science Dept, BBAU, Lucknow, India. 
Parul Rastogi, Research Scholar, Computer Science Dept, BBAU, Lucknow, India. 

29. Paper 20051029: Comparison of Traffic in Manhattan Street Network in NS2 (pp. 179-182) 

Ravinder Bahl, Rakesh Kumar, Department of Information and Technology, MMEC, Muallana, Ambala, 
Haryana, India 

Rakesh Sambyal, Information and Technology, MBS College of Engineering and Technology, Babliana, 
Jammu, Jammu and Kashmir, India 

30. Paper 25051036: An Evolving Order Regularized Affine Projection Algorithm, suitable for Echo 
Cancellation (pp. 183-187) 

Shifali Srivastava, Electronics Deptt, JUT, Noida, India 
M.C. Srivastava, Electronics Deptt, JUT, Noida, India 

31. Paper 30051060: Design and Implementation of Flexible Framework for Secure Wireless Sensor 
Network Applications (pp. 188-194) 

Inakota Trilok, Department of Computer Science & Engineering, National Institute of Technology 
Warangal, India 

Mahesh U. Patil, National Ubiquitous Computing Research Centre, Centre for Development of Advanced 
Computing, Hyderabad, India 

32. Paper 31051070: Optimizing the Application-Layer DDoS Attacks for Networks (pp. 195-200) 

P. Niranjan Reddy, K. Praveen Kumar, M. Preethi, 
KITS, Warangal, A. P. , India 

33. Paper 08051002: Survey - New Routing Technique for Grid Computing (pp. 201-206) 

R. Rameshkumar, Research Schlolar, J.N.T. University, Kukatpally, Hyderabad. 

Dr. A. Damodaram , Director/ U.GC Academic Staff College, J.N.T. University, Kukatpally, Hyderabad. 

34. Paper 27051037: A Forager Bees Behaviour Inspired approach to predict the forthcoming 
navigation pattern of online users (pp. 207-215) 

V. Mohanraj, Assistant Prof essor /IT, Sona College of Technology, Salem, Tamilnadu, India 

Dr. R. Lakshmipathi, Professor/EEE, St. Peters Engineering College {Deemed University), Chennai, 

Tamilnadu, India 

J Senthilkumar, Assistant Prof essor/TT, Sona College of Technology, Salem, Tamilnadu, India 

Y. Suresh, Assistant Prof essor/TT, Sona College of Technology, Salem, Tamilnadu, India 

35. Paper 27051038: Quality of Service Issues in Wireless Ad Hoc Network (IEEE 802.11b) (pp. 216- 
221) 

Mohammed Ali Hus sain, Research Scholar, Dept. ofCSE, Acharya Nagarjuna University, Guntur, A.P., 

India. 

Mohammed Mastan, Research Scholar, Dept. ofCSE, JNT University, Kakinada, A. P., India. 

Syed Umar, Research Scholar, Dept. ofCSE, Dravidian University, Kuppam, A.P., India. 



36. Paper 27051049: Collaborative Web Recommendation Systems based on Association Rule Mining 
(pp. 222-227) 

A. Kumar, Research Scholar, Department of Computer Science & Engineering, Sathyabama University, 
Chennai, India. 

Dr. P. Thambidurai, Department of Computer Science & Engineering, Pondicherry Engineering College, 
Puducherry, India. 

37. Paper 31051065: Similarity Based Imputation Method For Time Variant Data (pp. 228-232) 

Dr. F. Sagayaraj Francis, Saranya Kumari Potluri, Vinolin Deborah Delphin, Vishnupriya. B 
Pondicherry Engineering College, Pondicherry, India 

38. Paper 31051075: Efficient Node Search in P2P Using Distributed Spanning Tree (pp. 233-240) 

P. Victer Paul, T. Vengattaraman, M. S. Saleem Basha, P. Dhavachelvan 

Department of Computer Science, Pondicherry University, Puducherry, India. 

R. Baskaran, Department of Computer Science and Engineering, Anna University, Chennai, India. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 



Implementation of SPIN Model Checker for Formal 
Verification of Distance Vector Routing Protocol 



Kashif Javed 

Department of Information Technologies 

Abo Akademi University 

Turku, FIN-20520, Finland 

Kashif.javed@abo.fi 



Asifa Kashif 

Department of Electrical Engineering 

National University of Computer and 

Emerging Sciences, Islamabad, Pakistan 

asifa.ilyas 85 @ gmail.com 



Elena Troubitsyna 

Department of Information Technologies 

Abo Akademi University 

Turku, FIN-20520, Finland 

Elena.Troubitsyna@ abo.fi 



Abstract - Distributed systems and computing requires routing 
protocols to meet a wide variety of requirements of a large 
number of users in heterogeneous networks. DVR is one of many 
other employed protocols for establishing communication using 
routes with minimum cost to different destinations from a given 
source. Research work presented in this paper focuses on 
implementation of DVR in SPIN and provides formal verification 
of correctness of DVR behaviour covering all required aspects. 
Simulation results clearly show a proof of the established paths 
from each router to different destinations in a network consisting 
of six routers and a number of links. 

Keywords: Formal Verification, DVR Protocol, SPIN Model 
Checker, Distance Vector Routing, Implementation in PROMELA 



I. 



Introduction 



A computer network consists of a number of routers which 
have the capability to communicate with each other. Routing 
Information Protocol (RIP) is widely used for routing packets 
from a source to its destination in computer networks. RIP 
requires information about distance and direction from source 
to destination. Each router, in the Distance Vector Routing 
(DVR) methodology, keeps updated record of distances and 
hops of its neighbours. Various techniques are used to gather 
useful routing table information for each router. In one 
approach, special packets are sent by each router and are 
received back after having time- stamped by the receivers. 
Chromosomes have been employed in the Genetic Algorithm 
[1] to select the most optimal path by utilizing its fitness 
function, selection of next generation and crossover operation 
for updating the routing tables in an efficient manner. Thus, all 
routers keep refreshing their routing tables and maintain latest 
information about other neighbouring routers in order to 
provide optimized performance in the available network [1-3]. 

Mahlknecht, Madni and Roetzer [4] has presented an 
efficient protocol that uses hop count and cost information in 
its Energy Aware Distance Vector (EADV) routing scheme 
and makes use of shot-multi-hop routing for consuming lesser 
energy in the wireless sensor networks. EADV can do well for 
long lasting battery-powered sensor nodes while using the 
lowest cost path towards the selected sink node. An algorithm 
is considered the most effective if it contains the correct and 
latest information about its neighbours in its DVR table. An 



effort has been made by Liwen He by devising a computational 
method to protect a network from internal attacks (such as mis- 
configuration and compromise) through the use of verifying 
routing messages in the DVR protocols [5]. Formal verification 
of standards for DVR protocols has also been comprehensively 
presented by Bhargavan, Gunter and Obradovic [6] using three 
case studies. The researchers have used HOL ( an interactive 
theorem prover and SPIN (model checker) to verify and prove 
salient properties of DVR protocols. HOL and SPIN have been 
employed by these researchers for providing a proof of 
convergence for the RIP [7 ]. 

The remaining paper is organized as follows. DVR protocol 
is presented in Section II and Section III describes the use of 
SPIN tool and PROMELA language for formal verification. 
System design and implementation has been discussed in 
Section IV covering network topology, implementation details 
and operation of DVR protocol. Formal verification of 
simulation results has been illustrated in Section V and finally 
conclusions and future work is given in Section VII. 

II. Distance Vector Routing Protocol 

A. General Methodology 

A routing table is required to be maintained for each router 
in the network for the purpose of working of a DVR scheme. 
Routing table information is used to determine the best path 
(i.e. having minimum cost in terms of distance or hops) from a 
source to destination. Links are needed to connect concerned 
routers for establishing communication. An optimal DVR 
protocol has to exchange frequent messages in order to update 
the routing table of each router. So, exchanging information 
among neighbours is carried out on regular intervals. 

Routing table of every router keeps necessary information 
(i.e. id of neighbouring routers, most suitable outgoing link to 
be used for the destination, distance, hops (number of routers 
on the route), time delay, number of queued messages on the 
link). The process of making forwarding decision for selecting 
the best optimal path from source to destination is based on a 
combination of these parameters. The objective of routers is to 
send packets to hosts connected to the networks for 
heterogeneous requirements of a large number of users. In this 
way, efficient DVR schemes ultimately establish good global 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 



paths by connecting hosts in a distributed environment 
covering very long distances. Those routers are taken as 
neighbours which have links/interfaces to a common network. 

B. Routing Information Protocol 

RIP [8,9] is a widely used protocol for finding the optimal 
path to the destination in a network. Each router has a routing 
table and all routers periodically updated their routing tables by 
using advertising approach. All routes of a router are advertised 
through the mechanism of broadcasting RIP packets to all the 
neighbouring routers in the network. Every router checks the 
advertised information of neighbouring nodes and changes 
information only in its routing table if the new route to the 
same destination further improves the existing route length. In 
other words, the updated routing table information now takes to 
the best available route so far for the relevant destination. 

The number of hops in the RIP are kept low (up to 15) for 
the route length for faster convergence [6,7]. RIP methodology, 
however, prevents formation of loops between pairs of routers 
in order to minimize convergence time as well as permitted 
route length. Timer expiry record is also maintained in every 
routing table and is normally set to 180 seconds whenever a 
routing table is updated. As routers advertise after every 30 
seconds, the destination is considered unreachable if a router is 
not refreshed for 180 seconds. It further waits for another 120 
seconds. If the router remains un-refreshed during this time as 
well, then its route is removed from the routing tables of the 
concerned routers. This requirement is incorporated to cater for 
broken links, faulty networks and congestions. 



III. USE OF SPIN A N D PROMELA 



A. 



Formal Verification 

A number of new systems and methodologies are being 
devised by the researchers in different areas of science, 
technology and engineering as a result of meaningful R&D 
work being undertaken by academic and research institutes all 
over the world. Every proposed system requires a proof of its 
correctness by gathering results using simulation and testing 
techniques. Formal verification terminology [10,11] is in fact a 
process of actual demonstration of the system in order to check 
its correctness under the defined boundaries and valid 
conditions of used parameters/variables. 

Precision and accuracy of the system is verified by running 
the programming modules by employing required algorithms in 
the model checking approach. Errors occurred (if any) are 
properly identified under varying conditions so that such errors 
can be easily located by the users and are later on 
repaired/tackled by adjusting specifications of the model. 
Afterwards, the model description is fine tuned to achieve 
required model specifications for verification of correct results 
of the system. 

B. SPIN Tool and PROMELA High Level Language 

SPIN [12,13] is a open-source software tool and is widely 
used for the formal verification of software systems working in 



the distributed environment. Inspiring applications of SPIN 
include the verification of the control algorithms for various 
applications, logic verification of the call processing software 
for a commercial data communication, critical algorithms for 
space missions, operating systems, switching systems, 
distributed & parallel systems and formal verification of 
various routing protocols. This tool also supports interactive, 
random and guided simulations for a wide variety of 
applications. Spin can be used in four main modes (i.e. as a 
simulator, as an exhaustive verifier, as a proof approximation 
system and as a driver for swarm verification). 

Spin provides efficient software verification and supports 
the PROMELA (PROcess MEta LAnguage) high level 
language to specify systems descriptions [14]. It is a SPIN'S 
input language which is used to build detailed PROMELA 
models for complete verification of system designs. It provides 
a way for making abstractions of distributed systems. Different 
assumptions are used in SPIN to verify each model. After 
checking correctness of a model with SPIN, it can then be used 
to build and verify subsequent models of the system so that the 
fully developed system produces the required behavior. 
PROMELA programs consist of processes, message channels, 
and variables. 

IV. System Design and Implementation 

A. Network Topology 

The network topology shown in Figure 1 has been used for 
implementation of DVR protocol. There are six routers (A, 
B, C, D, E & F) and seven links (edges). Each link 
connects two routers. Weight values range from 2 to 23 for 
different links and these values indicate distances between 
routers. Integer values have been used and distance units 
can be chosen during actual implementation of the 
network. For example, the distance between routers A and 
C via B is 6 using 2 hops and via D, E and F is 33 using 4 
hops. 




23 




5 2 

Figure 1 : Network Topology 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 



Time O 




Is it anew 

shortest 

pawn? 



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path 




Distance calculation 
ace or di ng tv ti m e 



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Figure 2: System Flowchart 



5. System Implementation 

SPIN'S PROMELA language has been used to construct 
complete model of DVR protocol on a Pentium machine. 
Packets from the source to destination travel using links 
provided by routers by making use of their routing tables for 
the given distributed environment of the network. After 
initialization of the variables, distance is calculated from each 
router at time period T=0, T=l, T=2, T=3, T=4 and T=5. At 
each stage it checks whether the measured distance forms a 
new shortest path or not. Whenever the shortest path is found 
from the source to destination, routing table entry for the 
concerned router is automatically updated to make good 
forwarding decision in order to ensure optimal path, having 
minimum distance, for faster communication. Thus, each router 
updates its routing table after each time period. The main 
objective of the DVR protocol is to provide the current best 
route (path) from source to destination for each 



communication. Flowchart of the 
SPIN/PROMELA is shown in Figure 2. 



modeled system in 



For the given network, the PROMELA program has six 
processes (one for each time period) to find distance based 
upon the time period conditions (0 to 5). The found distance 
from a particular source to destination for each time period is 
compared with all the available alternate routes. Router's table 
is only updated if the new distance is minimum between the 
selected source and destination. The new shortest path is 
recorded after each calculation. If the determined route does 
not find minimum distance during the given time period, then it 
ignores its path without updating any entry in the routing table. 
Routers improve their routes whenever a router advertises its 
routing table to its neighbours. So, new routes are determined 
purely based on their length measured in distance. For timely 
convergence, the number of hops involved in the length are 
limited to 15 as already highlighted by Bhargavan et. al. [7]. 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 







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33 




A 














B 








33 






B 














B 












10 


C 














C 










10 




C 














D 




13 










D 














D 




13 








10 


E 














E 


31 




8 








E 














F 




9 




30 






F 














F 




10 










T=3 


A 














A 














A 




36 








13 


B 














B 














B 














C 




13 




33 






C 














C 














D 














D 






13 








D 














E 




11 










E 














E 




34 










F 














F 


33 












F 














T=4 


A 














A 






36 








A 














B 








36 






B 














B 












36 


C 














C 


36 












C 














D 




16 










D 














D 












36 


E 














E 














E 














F 








39 






F 














F 




36 










T=5 


A 














A 














A 














B 














B 














B 














C 














C 














C 














D 














D 














D 














E 














E 














E 














F 














F 














F 















Table 1: Calculated Distance from Routers A, B and C for Different Destinations at Time Periods T=0 to T=5 



C. Operation ofDVR Protocol 

DVR protocol works independently for every destination 
and it is assumed that there is no topology change for 
protocol's convergence during every time period. The router 
broadcasts after every 30 seconds and the destination is taken 
as inaccessible if it is not refreshed for 180 seconds. The route 
is removed from the tables of concerned routers if the 
particular router fails to refresh itself for 300 seconds. 

Although the PROMELA's built model can be used for any 
number of routers but its operation is restricted only to the 



topology given in Figure 1 . For the purpose of explanation of 
the model, it is assumed that every router operates without any 
problem and updates its routing table during regular intervals 
of time. 

At Time=0, it calculates distances to neighbouring routers 
from each router having maximum one hop. Thus, distance 
from A to B is 3 & A to D is 23from router A; from router B it 
is 3, 3 & 5 for routers A, C & E respectively; and distances are 
5, 5 & 2 for routers B, D & F respectively from router E. These 
distances can be observed in Tables 1 and 2. Now two hops 
from the current router are taken for T=l. So, distance from A 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 







Via 




Via 


Via 




FromD 


A 


B 


C 


D 


E 


F 


FromE 


A 


B 


C 


D 


E 


F 


FromF 


A 


B 


C 


D 


E 


F 


T=0 


A 


23 












A 














A 














B 














B 




5 










B 














C 














C 














C 






3 








D 














D 








5 






D 














E 










5 




E 














E 










2 




F 














F 












2 


F 














T=l 


A 














A 




8 




28 






A 














B 


26 








10 




B 














B 






6 




7 




C 














C 




8 








5 


C 














D 














D 














D 










7 




E 














E 














E 














F 










7 




F 














F 














T=2 


A 










13 




A 














A 






9 




10 




B 














B 








31 




8 


B 














C 


29 








10 




C 














C 










10 




D 














D 




31 










D 














E 


31 












E 














E 






11 








F 














F 




11 










F 














T=3 


A 














A 












11 


A 














B 










13 




B 














B 










33 




C 














C 








34 






C 














D 














D 














D 






16 




33 




E 














E 














E 














F 


32 








16 




F 














F 














T=4 


A 










16 




A 














A 






39 








B 














B 














B 














C 


36 












C 














C 










36 




D 














D 












34 


D 














E 


34 












E 














E 






37 








F 














F 








37 






F 














T=5 


A 














A 














A 














B 














B 














B 














C 














C 














C 














D 














D 














D 














E 














E 














E 














F 














F 














F 















Table 2: Calculated Distance from Routers D, E and F for Different Destinations at Time Periods T=0 to T=5 



to C via B is 6; A to E via D 28; and A to E via B is 8 as 
given in Table 1 . 

When T is taken as T=2, three hop lengths are counted for 
determining the distance from each router. From router D, 
measured distances are 13 via E to A, 29 via A to C, 10 via E 
to C and 31 via A to E. Same can be seen in Table 2. Hop 
length is four when T=3, distance covered to B via E, D via 
C and D via E is 33, 16 and 33 respectively from router F as 
shown in Table 2. Similarly, routes have distances of 36 (B 
via F), 36 (D via F) and 36 (F via B) from router C (for T=4) 
as given in Table 1. Both Tables 1 and 2 clearly indicate that 
no routes are available from any router when T=5 (six hops) 
for network configuration of Figure 1 . 



V. Formal Verification of Simulation Results 

The implemented system in PROMELA programming 
language has been tested exhaustively and obtained 
simulation results are shown in Tables 1 and 2. Spin model 
checker has been used to verify all the results. The developed 
model ensures that all the routers correctly maintain and 
update their tables as and when new routes are searched and 
visited. The broadcast mechanism works well at different 
time periods and the system provides correct and optimized 
results from each router to various destinations depending 
upon network topology, layout of routers and links 
connecting different routers in the network. 



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The SPIN'S verification model successfully checks all the 
available routes via different routers and permits only the 
shortest path from the available options. It is evident from 
the following decisions (only four out of many are presented 
here): 



1) At T =1, the route length from E to C via B is 8 
where as it is 5 via F. So, E router adopts F router's 
path to reach C. 

2) The distance between routers B &E via A and via C 
is 31 and 8 respectively. SPIN's checker confirms 
that minimum distance is covered for reaching to C 
from E when T=2. 

3) When T=3, the path cost determined by the model is 
13 from C to A via F, E & B but another path for 
connecting the same two router via B, E & D is 36, 
each path makes use of four hops. Of course, the 
longer path is simply ignored. 

4) Similarly, route length from F to D through C, B & 
A is 32 and it is 16 via routers C, B &E. A saving of 
16 is noted while using the most economical path. 

A careful analysis of the simulation results shown in 
Tables 1 & 2 clearly indicates that the modeled system in 
PROMELA operates correctly and provides the best possible 
routes involving minimum distances using DVR protocol on 
the given network environment. The system works 
efficiently under all conditions and the SPIN model checker 
has guaranteed correctness of all results. It means that all the 
routing tables are timely updated while messages are being 
sent to various destinations from a particular source. Now, 
this can be extended to bigger networks in the distributed 
environment for efficient and correct functioning using SPIN 
tool. 

VI. Conclusions and Future Work 

Many researchers have implemented DVR protocols for 
various applications. In this research work, PROMELA 
language has been used to implement DVR protocol on a six 
router model. Formal verification of DVR protocol 
properties has been shown through the use of SPIN checker 
model. The simulation results amply demonstrate correctness 
and reliability of DVR protocol under varying conditions. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
Performance of the implemented has been extremely well 
and it can further be improved to make it more efficient in 
terms of reducing storage space requirements, incorporating 
security mechanism for safer communication, minimizing 
congestion at peak loads and making it fault-tolerant for 
enhancing its reliability and flexibility. 



References 

[I] M. R. Masillamani, A. V. Suriyakumar, R. Ponnurangam and and 
G.V.Uma, "Genetic Algorithm for Distance Vector Routing 
technique", AIML International Conference, 13-15 June 2206, Egypt, 
pp. 160-163. 

[2] Andrew S.Tanenbaum, "Computer Networks", 4 th Edition,. Prentice- 
Hall Inc., 2005. 

[3] G. Coulouris, J. Dollimore and T. Kindberg, "Distributed Systems : 
Concepts and Design, 4th Edition, Addison- Wesley, 2005. 

[4] S. Mahlknecht, S. Madani and M. Rotzer, "Energy Aware Distance 
Vector Routing Scheme for Data Centric Low Power Wireless Sensor 
Networks," Proceedings of the IEEE International Conference on 
Industrial Informatics INDIN 06, Singapore, 2006. 

[5] Liwen He, "A Verified Distance Vector Routing Protocol for 
Protection of Internet Protocol", Lecture Notes in Computer Science, 
Networking - ICN 2005, Volume 3421, Springer, pp. 463-470. 

[6] K. Bhargavan, D. Obradovic and C. A. Gunter, "Formal Verification 
of Standards for Distance Vector Routing Protocols", Journal of the 
ACM, Vol. 49, no. 4, July 2002, pp. 538-576. 

[7] K. Bhargavan, C. A. Gunter, and D. Obradovic, "Routing Information 
Protocol in HOL/SPIN", Proceedings of the 13 th International 
Conference on Theorem Proving in Higher Order Logics 
2000, August 14 -18, 2000, London, UK, pp. 53-72. 

[8] C. Hendrick, "Routing Information Protocol", RFC 1058, IETF, June 
1988. 

[9] G. Malkin, 'RIP Version Carrying Additional Information', IETF 
RFC 1388, January 1993. 

[10] J. Katoen, "Concepts, Algorithms and Tools for Model Checking", 
Lecture Notes 1998/1999, Chapterl: System Validation. 

[II] N. A. S. A. Larc, "What is Formal Methods?", 
http://shemesh.larc.nasa.gov/fm/fm-what.html, formal methods 
program. 

[12] R. de Renesse and A. H. Aghvami "Formal Verification of Ad-Hoc 
Routing Protocols using SPIN Model Checker", Proceedings of IEEE 
MELECON'04, Croatia, May 2004. 

[13] G. J. Holzmann, "The Model Checker SPIN", IEEE Transactions on 
Software Engineering, Vol. 23, No. 5, May 1997, pp. 279-295. 

[14] G. J. Holzmann, "Design and Validation of Computer Protocols", 
Prentice Hall, November 1990. 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 



Integrated Queuing based Energy-Aware Computing 

in MANET 



Dr. P.K. Suri, Dean, Faculty of Sciences, Dean, 

Faculty of Engineering, Professor, Deptt. of Comp. 

Sci. & AppL, Kurukshetra University 

Kurukshetra, Haryana, India 

pksurikuk@rediffmail.com 



Kavita Taneja, Assistant Professor 

M. M. Inst, of Computer Tech. & Business Mgmt. 

Maharishi Markandeshwar University 

Mullana, Haryana, India 

kavitatane@gmail.com 



Abstract — Mobile Computing has witnessed a flare-up of 
applications in mobile and personal communication. It is a 
phrase that embodies on-the-go business initiatives. By marrying 
today's dominant office computing environment with 
increasingly compact-but-powerful handheld devices, mobile 
computing makes it possible for millions of workers to conduct 
business on their feet and from the road. Energy efficiency is an 
important design consideration due to the limited battery life of 
mobile devices. In order to minimize energy consumption and 
maximize the network life time, the proposed simulator opt for 
intelligent routing through substitute routes instead of 
conventional routing through shortest route. To reduce energy 
consumption only devices in the traversed route are active, other 
mobile devices of network are switched off. Also simulator 
implements clusters for efficient sleep and active mobile device 
mechanism and reflects on MANET in terms of queuing network 
and consider the packets arrival rate in terms of poisson 
distribution. 

Keywords- Queuing theory, poisson distribution, clustering, 
mobile unit, mobile ad hoc network. 

I. Introduction 

MANET (Mobile Ad Hoc Network) is a group of mobile units 
(MUs) that instantly form a network among themselves 
without any fixed infrastructure. The mobile computing offers 
users in such open networks, the on the fly access to 
information. Due to the explosion of wireless and portable 
devices, such as cellular phones, PDAs (Personal Digital 
Assistants), palm computers, user is spoilt to enjoy the 
freedom and convenience in their daily lives [1]. In addition, 
the race against time and enhancements in the current 
MANETs make user more expected to do things through the 
Internet, such as online banking, online shopping etc. Then 
there is no doubt user's demand for information access in 
mobile environment increases spectacularly [2]. Since the 
mobile computing is relatively novel to its big brother- 
desktop computing, the resource in the mobile computing 
world is relatively restricted. Also constraints, such as limited 
hardware resources especially bounded battery life, stochastic 
topology hence uncertain device location, etc, make 
challenges for optimally utilizing resources. Increasingly, 
power consumption within such open networks is becoming a 



core issue for the low-power mobile devices [3]. For 
example, soldiers in a battlefield can have handheld mobile 
devices to communicate with each other, or in emergency 
situations like earthquakes where the existing infrastructure 
has been destroyed, an ad hoc network can instantly be 
deployed to aid in disaster recovery, meetings or conference in 
which persons wish to quickly share information, and data 
acquisition operations in inhospitable terrains. In order to 
crack shortages in mobile computing for smooth satisfaction 
of the emerging needs, the enhanced computing needs to be 
adaptive [4]. The primary goal of routing protocol in MANET 
is correct and efficient route establishment between a pair of 
MUs so that packets can be delivered in a timely manner with 
minimum battery consumption for longer network 
connectivity. In the evaluation of any algorithm for energy 
conservation, an estimate of energy consumption is necessary 
[5]. 

The energy is consumed per MU in two phase as 
shown in Eq. (1), the energy used in route discovery and 
amount of energy used in packet transmission. 



-Total . 



-Route-Discovery 



-Packet- Transmission 



x^ioiai _ x^Kouie-LMScovery , y^racKei-iransmission /i \ 

During packet transmission a typical MU may exist in any one 
of the four modes: 

a) Sleep mode: Sleep mode has very low power 
consumption. The network interface at a MU in sleep 
mode can neither transmit nor receive packets; the 
network interface must be woken up to idle mode first 
by an explicit instruction from the MU. 

b) Idle mode: In this mode MU is neither transmitting nor 
receiving a packet. This mode still consumes least 
amount of power because the MU has to listen to the 
wireless medium continuously in order to detect a 
packet that it should receive, so that the MU can then 
switch into receive mode. 

c) Receive mode: In this mode a MU actually receives a 
packet. 

d) Transmit mode: In transmit mode a MU uses its 
maximum power level to transfer packet to another 
MU. 



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Receive and idle mode require similar power, and transmit 
mode requires slightly greater power [6]. Sleep mode 
requires more than an order of magnitude less power than 
idle mode. These measurements show that the network 
interface expends similar energy, whether it is just listening 
or actually receiving data. Hence, switching to sleep mode 
whenever possible is a wise decision on the part of MU that 
will lead to momentous energy savings. The sources of 
power consumption, with regard to network operations, can 
be classified into two types: (a) communication related, and 
(b) computing related. Communication involves the cost of 
sending data along with control packet from source MU, 
routed through intermediate MU and cost at receiving at 
destination end. It mainly deals with the cost of routing in 
network or in route establishment. Whereas computing 
mainly involves the cost of using CPU and main memory, 
disk drives and other components of computer for 
calculating computing related cost [7]. If we take the 
shortest route to deliver the message it is not the good way 
of energy conservation in network. As a group of MUs 
coming under the shortest route is used rapidly as compared 
to other MUs, so their power decreases rapidly and may a 
situation come that they are having no power. In that 
situation we refer the substitute path routing [8]. The 
substitute routes and clustering approach ensures that the 
energy of a group of MUs is used at a particular instant in a 
respective way [9]. By this all MUs of a network take active 
participation in route selection; overall energy expenditure 
of a network is minimized, causing maximization of 
network life. The remainder of this paper is organized as 
follows. The basic concept of queuing theory and MANETs 
is explained in section II. Section III gives the Simulator for 
finding substitute route and clustering approach in MANET. 
Section IV gives the probabilistic approach of finding the 
number of sleep MU and active MU. Finally we conclude 
our paper in section V. 

II. Queuing Theory And Manet 

We consider that inter arrival times of packets at a MU follow 
a discrete poisson distribution in which an event occurring 
exactly k times during an interval t is given by a probability 
mass function 



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Vol 8, No. 3, 2010 
We generate Ti's according to Eq. 4 and keep on adding them 
till their sum exceeds 1 and the count gives the Poisson sample 
(k). At any given time t, the probability of a MU busy in 
packet processing is given by "(5)". 



g k (t) = (a) k (l/k!)e" t 



(2) 



where X is the average number of times the event occurs in a 
unit period. In our network the packet is always transferred 
from source to destination, we consider our source as a server. 
With single server, service times follow negative exponential 
distribution. Packets are served in FIFO order. Pseudo 
random numbers are generated and using % 2 test, we have 
samples (T) from an exponential distribution with specified 
expected value l/X as 



RN=RNDY1(DUM), 
= -1.0A*ALOG(RN). 



(3) 
(4) 



p = p / a. 



(5) 



T£ji 



<~Packet queue- -> 




Figure 1 Queuing theory embedded in MANET 

Considering computation of Packet queue length of each MU 
as shown in figure 1 , if more packets passed through Packet 
queue it is lost, here Packet queue limit is n. Because packet 
arrival at a MU is highly stochastic, hence simulator design 
assumes that the Packet queue length is such that it is 99% of 
the time sufficient to hold the packets waiting to be processed. 
Thus the probability of having more than n packets in wait for 
MUis 

(6) 



p n+1 =0.01. 



In "(6)", n gives the long enough Packet queue length with 99 
per cent assurance. For efficient routing in order to lessen the 
number of packets loss we suggest that the packet to be 
delivered in substitute routes instead of shortest route. So 
initially the numbers of all possible substitute routes are to be 
calculated. Then while packets traveling through a particular 
route the MUs of other routes to be switched off if they are not 
doing any useful work in order to minimize energy 
consumption. The algorithm describing this substitute route 
finding procedure is given in section III. 

III. Algorithm for substitute route And Clustering 
Approach. 

In this section we propose our algorithm to find out substitute 
routes in a network to maximize network life if direct route 
between source and destination does not exist [10]. Initially 
the MUs are set up in an area to establish network 
connectivity. Then we go for substitute route finding 



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procedure, by running this algorithm in a network we keep 
track of all substitute routes which does not contain any 
duplicate MUs. By taking the substitute routes traffic load is 
shared and congested routes are avoided which may cause in 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
required amount of energy it can elect the MU with huge 
resource as CH in order to maintain network connectivity. 



Algorithm for substitute route ( ): 

1. Initialize all MUs of network to READY state from 
SWITCH OFF state at time of deployment of network. 

2. Put the source MU (S) in Packet queue. 

3. Repeat step 4 until Packet queue is empty. 

4. Process front MUs of Packet queue by adding its 
neighbor's to Packet queue where each entry is unique 
along with that keep track of their parent MU. 

5. End of step 3. 

6. As destination (D) is reached stop and find the route 
traversing from 'D' in a reverse order by tracking the 
origin till 'S' at origin is reached. 

7. SWITCH OFF all MUs coming in the route for finding 
another route without any duplicate MU. 

7. Go to step 4 

8. Exit. 



Figure 2. Algorithm: Substitute Route 

retransmission due to collision. Since no MU is duplicated, all 
MUs take in active participation in route selection. No MU is 
penalized more as compare to other MUs in network. So there 
is no network partition which causes maximization of network 
life [4, 11, 12]. Before going to the algorithm find out list of 
neighbors of all MUs. For evaluation of algorithm a priority 
Packet queue is considered. Considering that the front MU of 
a Packet queue has to be processed first as it has highest 
priority than other. The network is divided level wise. Each 
level is forming a cluster [6, 13]. Each cluster is assigned with 
a cluster head (CH). A MU is selected as a CH having more 
battery energy [14] which can communicate to its neighbor in 
one hop distance. The lower level of network which is nearer 
to the source MU keeps track of all substitute routes. All the 
CHs which are formed in a network can exchange their 
messages in between them at any point of time by 
broadcasting messages in between the CHs. The CH in each 
cluster broadcasts the control message to its entire MUs. This 
control message contains the information about MU ID, and 
residual energy. When packet starts transferring in one route, 
the CH of initial level make the MU taking part in route 
selection as ACTIVE and make other MUs to be in SLEEP 
mode. It broadcasts the message to all the CHs. All the CH 
makes the MUs in that path active and other to be in sleep 
mode. When the next packet starts traversing it transfer 
through another route. Then the CH positively play their role 
to make the MU falling in that route to ACTIVE and other 
MUs of the cluster to be SLEEP mode. Using this when 
packets transferring in a particular route, the MUs of that route 
are in ACTIVE state where all other MUs of a network are in 
SLEEP mode. So this lessens the overall energy consumption 
of whole network. At any point of time if CH is not having 



CH: Cluster Head 
D: Destination 
S: Source 




Figure3 Clustering in MANET 

IV. The Probabilistic Approach 

To find out the number of MUs to be ACTIVE and the number 
of MUs to be in SLEEP MU, we take the following 
probabilistic approach. Let T be the total number of MUs, A 
be the number of ACTIVE MUs and S be the number of MUs 
in SLEEP mode. Now we find the joint probability of 'a' MUs 
among the total MUs active and 's' MUs among total MUs in 
sleep mode. The packet arrival distribution follows Poisson's 
distribution (k). Let probability of a MU being ACTIVE 
mode= P. The probability of a MU being in SLEEP mode= 1- 
P. For the discrete random variable, the joint probability mass 
function 

P {A=a and S=s} = P{S=s | A=a}*P{A=a} 

= P{A=a|S=s}*P{S=s}. (6) 

Also, SSP{ A=a and S=s}=l, Since A and S are independent 
P{A=a and S=s}= P{A=a}* P{S=s} 

= (3L a e^/a!)*(3LVVs!). (7) 

It shows that probability of 'a' number of MUs in ACTIVE 
state and V number of MUs in SLEEP state. By putting the 
value of probability to 1 and specifying the rate of arrival X we 
find the ratio of number of ACTIVE MUs and SLEEP MUs, 
which effectively measure the energy consumption rate. As by 
the MU going into SLEEP state it consume less energy, 
therefore as the number of MUs not doing any useful work in 
the system going into SLEEP state, then the energy 
conservation will increase, which enhance our system 
performance with maximized network lifetime. 

V. Conclusion 

In this paper we give a brief study about role of queuing 
theory in MANET. As all MUs are battery operated, and 
battery power is restricted we go for proficient use of this in 
order to minimize energy consumption and maximize network 



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life [15]. The today's user rely on mobile computing for a 
multitude of operations as is equipped with range of devices 
like notebook computers, personal digital assistants and other 
communication hungry portable stuff, even kitchen of 
homemakers is gripped with spectrum of wireless interfaces 
for networked communication [16]. By using the substitute 
route and clustering technique we try minimize energy 
consumption of overall network. Also switching some of MUs 
to SLEEP mode we try to further minimize energy 
consumption in network. To calculate the number of ACTIVE 
MUs and SLEEP MUs in network we are using the 
probabilistic approach. This model promises to provide 
stochastic handling equipped design platform for extension to 
an energy efficient protocol for MANET. 

References 

[I] Sarkar, S. K., T.G. Basavaraju, and C. Puttamadappa, "Ad Hoc Mobile 
Wireless Networks: Principles, Protocols and Applications", Auerbach 
Publications, 2007. 

[2] Do-hyeon Lee , Song Nan Bai , Jae-il Jung, "Enhanced next hop 
selection scheme for QoS support in multi-hop wireless networks", 
Proceedings in the 2009 International Conference on Hybrid Information 
Technology, pp.587-593, August 27-29, 2009, Daejeon, Korea. 

[3] D. Kim, J. Garcia and K. Obraczka, "Routing Mechanisms for Mobile 
Ad Hoc Networks based on the Energy Drain Rate", IEEE Transactions 
on Mobile Computing. Vol 2, no 2, 2003, pp.161-173. 

[4] Thomas Kunz, "Energy-efficient routing in mobile ad hoc networks: a 
cautionary tale", International Journal of Autonomous and Adaptive 
Communications Systems, Vol.2 no.l, pp. 70-86, March 2009. 

[5] L. Feeney and M. Nilsson, "Investigating the Energy Consumption of a 
Wireless Network Interface in an Ad Hoc Networking Environment", 
Proceedings in INFOCOM 2001, Vol. 3, pp. 1548-1557, Anchorage, 
Alaska, April 2001. 

[6] C. E. Jones, K. M. Sivalingam, P. Agrawal and J. C. Chen, "A survey of 
Energy Efficient Network Protocols for Wireless Networks", Journals in 
Wireless Networks, Vol. 7, no. 4, pp. 343-358, August 2001. 

[7] M. Tarique, K. E. Tepe and M. Naserian, "Energy Saving Dynamic 
Source Routing for Ad Hoc Wireless Networks", Proceedings in Third 
International symposium on Modelling and Optimization in Mobile Ad 
Hoc, and Wireless Networks, Canada, pp. 305-310, April 2005. 

[8] C.K. Toh. "Maximum Battery Life Routing to Support Ubiquitous 
Mobile Computing in Wireless Ad Hoc Networks", IEEE 
Communications Magazine, Vol 39, no 6, 2001, pp. 138-147. 

[9] E. M. Royer and C. K. Toh, "A Review of Current Routing Protocol for 
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Communication Magazine, Vol. 6, no. 2, pp. 46-55, April 1999. 

[10] M. Tarique, K. E. Tepe and M. Naserian, "Energy Saving Dynamic 
Source Routing for Ad Hoc Wireless Networks", Proceedings in Third 
International symposium on Modelling and Optimization in Mobile Ad 
Hoc, and Wireless Networks, Canada, pp. 305-310, April 2005. 

[II] K. Pappa, A. Athanasopoulos, E. Topalis and S. Koubias, 
"Implementation of power aware features in AODV for ad hoc sensor 
networks. A simulation study", Proceedings in IEEE conference on 
Emerging Technologies and Factory Automation, Rome, pp. 1372-1375, 
September 2007. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 

[12] C. Jie, C. Jiapin and L. Zhenbo, "Energy- efficient AODV for Low 
Mobility Ad Hoc Networks", Proceedings in IEEE International 
Conference on Wireless Communications, Networking and Mobile 
Computing, Shanghai, pp. 1512-1515, September 2007. 

[13] S. Soro and W. B. Heinzelman, "Cluster head election techniques for 
coverage preservation in wireless sensor networks", Proceedings in 
Elsevier Journal Ad Hoc Networks, Vol. 7, no. 5, pp. 955-972, July 
2009. 




[14] X. Hou, D. Tipper and S. Wu, "A Gossip-Based Energy Conservation 
Protocol of Wireless Ad Hoc and Sensor Networks", Journals of 
Networks and Systems Management, Computer Science, Springer New 
York, Vol. 14, no. 381-414, September 2006. 

[15] Zhao Cheng, Wendi B. Heinzelman, "Discovering long lifetime routes in 
mobile ad hoc networks", Ad Hoc Networks, Vol.6 no. 5, pp. 661-674, 
July 2008. 

[16] Helmut Hlavacs , Karin A. Hummel, Roman Weidlich , Amine M. 
Houyou, Hermann De Meer, "Modelling energy efficiency in distributed 
home environments", International Journal of Communication Networks 
and Distributed Systems, Vol.4 no. 2, pp. 161-182, January 2010. 



Dr. P. K. Suri received his Ph.D. degree from 
Faculty of Engineering, Kurukshetra University, 
Kurukshetra, India and Master's degree from Indian 
Institute of Technology, Roorkee (formerly known as 
Roorkee University), India. Presently He is Dean, 
Faculty of Sciences, Dean, Faculty of Engineering, 
Kurukshetra University and is working as Professor in 
the Department of Computer Science & Applications, 
Kurukshetra University, Kurukshetra, India since Oct. 
1993. He has earlier worked as Reader, Computer Sc. & Applications, at 
Bhopal University, Bhopal from 1985-90. He has supervised six Ph.D.'s in 
Computer Science and thirteen students are working under his supervision. He 
has more than 110 publications in International / National Journals and 
Conferences. He is recipient of 'THE GEORGE OOMAN MEMORIAL 
PRIZE' for the year 1991-92 and a RESEARCH AWARD -"The Certificate 
of Merit - 2000" for the paper entitled ESMD - An Expert System for 
Medical Diagnosis from INSTITUTION OF ENGINEERS, INDIA. His 
teaching and research activities include Simulation and Modeling, SQA, 
Software Reliability, Software testing & Software Engineering processes, 
Temporal Databases, Ad hoc Networks, Grid Computing and Biomechanics. 

Kavita Taneja has obtained M.Phil(CS) from 
Alagappa University, Tamil Nadu and Master of 
Computer Applications from Kurukshetra 
University, Kurukshetra, Haryana , India. Presently, 
she is working as Assistant Professor in M.C.A. at 
M.M.I.C.T & B.M., M.M. University, Mullana, 
Haryana, India. She is pursuing Ph.D in Computer 
Science from Kurukshetra University. 

She has published and presented over 10 
papers in National/International journals/conferences and has bagged BEST 
PAPER AWARD, 2007 at International Conference for the paper entitled 
"Dynamic Traffic -Conscious Routing for MANETs" at DIT, Dehradun. She 
has supervised five M.Phil scholars in Computer Science. Her teaching and 
research activities include Simulation and Modeling and Mobile Ad hoc 
Networks. 




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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 



A Review of Negotiation Agents in e-commerce 



Sahar Ebadi 

Department of Information System, 

Faculty of Computer Science and 

Information Technology, University 

Putra Malaysia, Serdang, Malaysia 

Sah_ebadi@yahoo.com 



Md. Nasir Sulaiman 

Department of Information System, 

Faculty of Computer Science and 

Information Technology, University 

Putra Malaysia, Serdang, Malaysia 

nasir® fsktm.upm.edu. my 



Masrah Azrifah Azmi Murad 

Department of Information System, 

Faculty of Computer Science and 

Information Technology, University 

Putra Malaysia, Serdang, Malaysia 

masrah. azrifah @ gmail .com 



Abstract — With the growth of World Wide Web and the 
increasing human demand on online trading, there is a pressing 
need for complex systems that are capable of handling the client 
needs in e-commerce. In recent years, numbers of Multi Agent 
System (MAS) developers arise to fulfill this mission by 
performing a huge number of studies on agent negotiation 
systems. However, far too little attention has been paid to 
provide a rich review as a repository for developers to distinguish 
the aspect and scope of MAS. The purpose of this paper is to do 
a review of progressing agent negotiation technology in e- 
commerce. In order to achieve our aim we propose different 
classification schemata and interpret different models according 
to the proposed classifications. Popular methods for optimizing 
negotiation agents have been introduced and the effect of relative 
techniques has been analyzed. The result of analysis shows that 
genetic algorithm is the most effective learning technique in 
optimizing negotiation models. Moreover, we interpret the most 
prominent negotiation models according to the main parameters 
on which any negotiation agent model depends. The result of 
these analysis supplies a resource of differentiating competing 
alternatives for the area of negotiation agent's models to exploit. 
Finally, a range of open issues and future challenges are 
highlighted. 

Keywords-component; Artificial Intelligence; Agent; Multi- 
Agent System; Negotiation; e-Commerce 

I. Introduction 

With the rapid growth of the World Wide Web and huge 
demand of online trading every day, there is a need for 
complex systems that are capable of addressing the online 
trading needs of human. Such systems must be capable of 
establishing communications, making decisions and handling 
customer's requirements. Many researchers in Multi Agent 
System (MAS) and agent negotiation systems succeed to fulfill 
these obligations on e-commerce. 

As the number of research conducted on agent negotiation 
development has rapidly increased, the need of conducting a 
comprehensive review on negotiation agent in e-commerce has 
increased. So far, however, there has been little review about 
agent negotiation models specifically in e-commerce. The 
purpose of this paper is to review studies conducted on 
negotiation agent systems in e-commerce (online trading). In 
general, lack of universally accepted definitions in negotiation 
agent systems is one of the difficulties in this area. Therefore, 
we initially clarify the negotiation agent system's 



characteristics and concepts used in this field. Then we 
explicate the most popular methods in optimizing negotiation 
agent models. Related prominent techniques will be explained 
and some exemplar stand out models will be interpreted. 

This paper presents two novel classifications, namely 
classification according to the type of agents, and classification 
according to the attitude of agents; in addition to the 
Wooldridge's popular classification according to the number of 
agents involved in negotiation. These classifications help 
developers and researchers to have a better understanding of 
aspects and scope of agent negotiation systems. These features 
can facilitate future developments on negotiation mechanism in 
MAS. Finally, we will trace the progress of negotiation 
systems generations by analyzing the most prominent works 
during the last decade, from early 1996 to late 2009. In this 
work, we will interpret the whole negotiation agent model 
according to four important characteristics of the system so- 
called negotiation protocol, negotiation strategy, agent 
characteristics and negotiation setting. In addition, the most 
effective learning technique will be verified through 
interpreting the final utility of exemplar models. The remainder 
of this article is structured as follows. Section 2 presents 
essential characteristics and concepts in the scope of our work. 
Section 3 presents popular methods of optimizing negotiation 
models and a brief description of the relative techniques. In 
section 4, proposed classifications are discussed. Section 5 
draws together the two later strands. It discusses some 
exemplar negotiation agent-based applications and highlights 
new direction to the future works. Section 6 presents the 
conclusion of this paper. 

II. Concepts and Characteristics 

As mentioned earlier, one of the difficulties in agent 
negotiation systems is the lack of universally accepted 
definitions. In order to draw a clear picture of concepts and 
characteristics of negotiation agents, it is important to recap 
some key concepts and definitions which are accepted by some 
experts in this field. 

Agent: an agent is a computer system that is situated in 
some environment, and is capable of autonomous action in this 
environment in order to meet its designed objective [1]. 
According to Wooldridge and Jennings [1], the term agent is 
most generally used to denote a hardware or (more usually) 



* Responsible author 



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software-based computer system that enjoys the following 
properties: 



autonomy: agents operate without the direct 
intervention of humans or others, and have some kind 
of control over their actions and internal state [2] 



social ability: agents interact with other agents (and 
possibly humans) via some kind of agent- 
communication language [3] 



reactivity: agents perceive their environment, (which 
may be the physical world, a user via a graphical user 
interface, a collection of other agents, the 
INTERNET, or all of these combined), and respond in 
a timely fashion to changes 



• pro-activness: agents do not simply act in response to 
their environment, they are able to exhibit goal- 
directed behavior by taking the initiative. 

In most of the real world problems, agents need to interact 
with other agents to achieve their objectives. Many problem 
cases are innately multi party or social such as negotiation 
scenarios. Many are more complex to be solved by an agent 
(e.g. monitoring and performing an electronic marketplace). In 
these cases, multi agent systems are designed to address these 
issues. 

Multi Agent System: generally Multi Agent Systems (MAS) 
refers to such a system that many intelligent agents involved 
interact with each other in a selfish or cooperative manner. In 
MAS, agents are autonomous entities and can cooperate to 
reach a common goal (e.g. an ant colony) or just follow their 
own preferences (as in the free market economy). 

According to Sycara [4], "the characteristics of MASs are 
that (1) each agent has incomplete information or capabilities 
for solving the problem and, thus, has a limited viewpoint; (2) 
there is no system global control; (3) data are decentralized; 
and (4) computation is asynchronous." 

Since trading domains are often bounded by limited 
resources and abilities, negotiation has become an essential 
activity in e-commerce applications. 

Negotiation: negotiation is a process in which two or more 
parties with different criteria, constraints, and preferences, 
jointly reach to an agreement on the terms of a transaction [5]. 

Negotiation Agents : according to Raymond [6], the notion 
of agency can be applied to build robust, fast and optimal 
architecture for automated negotiation systems within a group 
of software agents communicating and autonomously making 
decisions on behalf of their human users . 

After a clear understanding of what agent-based concepts 
are, it is necessary to emphasize the key concepts on 
optimizing negotiation agent systems on e-commerce. 



III. 



Vol. 8, No. 3, 2010 
NEGOTIATION AGENT'S CLASSIFICATION 



Negotiation Agents can be categorized by severed 
orthogonal dimensions. In this work, we present three 
classifications. The first one is a popular classification 
introduced by Wooldridge [5]. The second and third 
classifications are proposed by this work. Proposed 
classifications schemata are done according to i) the types of 
agents involved in negotiation and ii) the attitudes of agents 
involved in negotiation. 



A. Number of Agents Involved in the Negotiation 

This category is one of the most clear and popular 
categories which are divided into three groups named one-to- 
one negotiation, one-to-many negotiation and many-to-many 
negotiation. 

One-to-one negotiation is suitable in situations where one 
agent is negotiating with another agent (e.g. Kasbah model 
proposed by Anthony et al. [7] and Ryszard Kowalczyk [8]) in 
case where one agent is involved in the negotiating with other 
agent. This is a simple but basic kind of negotiation on e- 
commerce. 

One-to-many negotiation is when one agent negotiates with 
several agents as its opponents. This kind of negotiation, in 
fact, is originated from several combinatorial one-to-one 
negotiations. A practical example of a one-to-many 
negotiation is auction system where several bidders participate 
in an auction at the same time. Many researchers [9, 10] on 
online trading propose their models based on this classification. 

Many -to-many negotiation as described by Wooldridge [5], 
is when many agents negotiate with many other agents 
simultaneously. Jiangbo many-to-many negotiation framework 
is one of the best examples of this category [11-13]. 



B. Type of Agents Involved in Negotiation 

We believe finding the suitable classification of opponent 
agents type will help agents to choose the best possible tactics 
to deal with their opponents over a particular good. We think 
this classification will decrease the negotiation cost by 
improving the accuracy of agent's beliefs. Many researchers 
[10, 14, 15] have discussed types of agents involved in a 
negotiation. According to the variety of application different 
concepts are offered. Nguyen [10] divided agents involved in 
negotiation into two groups according to the amount of the 
concede which they are willing to give. The first type is 
conceder and the second is non-conceder. The conceder is 
referred to the agents that are willing to concede with the aim 
of selling. In contrast, non-conceder agents are those who just 
deal with a situation where there is some amount of benefit. 
Bayesian classification was employed to identify the probable 
types of agents. Then, agents try to modify appropriate 
strategies according to the type of the opponent agents. 

A new direction in this area is to apply some sense of 
machine learning techniques to increase accuracy of the 
opponent agent classification. Therefore, agent is able of 
making a better deal that improves the performance of the 
model. 

Ros and Sierra [16] defined 5 types of agents that have been 
experienced by the model and finally evaluated by utility 



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product and utility difference. These 5 types of agents so- 
called NegoTo agent, Random agent, Alternate agent, TOagent 
and Nego agent are differentiated by tactics and behaviors they 
obey. These 5 types of agents are explained In a nutshell. 
NegoTo agent, before reaching deadlock applies trade-off 
tactic, followed by negoEngin tactic. Random agent chooses 
the next tactic randomly (e.g. negoEngin, trade-off, trade-off 
and negoEngin). The above-mentioned agents alter negoEngin 
and trade-off tactics one at a time. TOagent obeys trade-off 
tactics when utility of new offer is higher than the previous 
one, otherwise, the aspiration level is decreased and new offer 
is proposed. Nego agent only follows negoEngin tactics during 
the negotiation. 

We think that applying a decision making model that 
discovers the highest utility among all tactics can increase the 
performance of the system. In addition, applying the previous 
history or knowledge extracted from past experience (e.g. what 
happened in the past when we used a specific tactic in the same 
situation?) in the same situation can help agents decision 
making. This will result in a more accurate choice of tactics or 
type of agents to negotiate and end in a higher chance of 
reaching agreement. 



C. Attitude of Agents Involved in Negotiation 

Internet is populated by many agents coming from different 
sources with different attitudes and goals. Some researchers 
mentioned that, although agents can be categorized by their 
behaviors, they can also be categorized according to their 
attitudes toward their goals. In a clear word, agent's attitude 
defines how an agent selects beneficial opponent in a given 
different situation[17]. Many researchers are working on this 
area in order to reach to a better efficiency in their models [10, 
15, 17, 18] . In Ikpeme proposed model [15] agents are divided 
according to their social attitudes into three different groups so- 
called helpful, reciproactive, and selfish. Helpful agents are 
those who are willing to help. This group benefits in the 
homogenous groups of helpful agents. Reciproactive agents 
evaluate the request and will accept opponent's request if the 
agent meets a balance of cost and saving, otherwise they reject 
the request. This group has the best performance when situated 
in an open group of agents. Selfish agents never help the other 
agents. So, they always benefit when situated among helpful 
agents but rarely benefit from reciproactive agents. Finally, the 
results show that in an open environment success rate is many 
times better for reciproactive agents than selfish agents. 

In the work presented by Jaesuk et al. [17], agents attitude 
defines the priority that an agent places on various choices it 
may have regarding member selection. So, agent's attitude is 
(1) toward rewards or (2) toward risk. Agent attitude toward 
reward is the agent's point of view toward finding the best 
opponent. The attitude considers the opponent's quality of 
service. Agent attitude toward risk is agent sensitivity to the 
possible risk of opponent agent (which depends on unreliability 
and unavailability of agents). The result of the research shows 
that agents with strong attitude toward risk are more beneficial 
when there is a higher chance of failing jobs due to tight time 
or low availability and reliability of opponent. But, agents with 
the high attitude toward quality situated in a case where time is 
enough and the penalty value is small are able to earn more 
benefit. Also, in some cases agent's classification is done 



Vol 8, No. 3, 2010 
according to the agent's behavior as proposed by Ikpeme et al. 

[15]. 

The proposed classifications will clear the roadmap for 
developers in establishing new research in the area of 
negotiation agent systems. Once developers define the agent 
classification schema, automatically dimensions and scopes of 
research area are clarified. Simply, by following previous 
works conducted in the specified class, the gaps and 
disadvantages of research area will be highlighted. This will 
assist researchers to develop and maintain innovative research 
in the area of negotiation agent models. 

IV. Popular Methods of Optimizing Negotiation 

Systems 

Designing an optimized agent negotiation system is one of 
the important issues addressed recently by many researchers 
[16, 19-23]. In recent years, a great deal of effort has been 
devoted toward optimizing negotiation agents [24-27]. Mostly 
this aims at improving agent i) adaptability, ii) intellectuality, 
iii) applied strategies, and iv) gathering information. 

A. Adaptability 

Negotiation agents are situated in open environment where 
new agents may come and some agents may leave the 
environment. Agents are characterized by deadlines, volatile 
preferences, and incomplete information. In such situations, 
agents must survive by changing their strategies, preferences 
and even learning opponent's preferences and behaviors. This 
attempt to change agent's behaviors, preferences, and strategies 
toward reaching an agreement is called adaptability. This 
method aims to find the highest satisfactory offer for agents as 
well as being acceptable for opponent agent. Many researchers 
[6, 27, 28] applied this methods for optimizing negotiation 
systems, as it is effective and necessary to assist the autonomy 
aspect of agents. Zhang et al. [27] proposed a new adaptive 
negotiation strategy which assists negotiation models by 
enhancing the adaptability of agents. In this scenario, an agent 
uses adaptive strategy to estimate the strategies and preferences 
that opponent agents used in the last few offers in negotiation. 
So, an agent can choose appropriate negotiation factors to 
adjust its strategy. Even after choosing the strategy, there is a 
chance to change the parameters of chosen strategy to adapt 
dynamically to the strategy of another agent. Also, Raymond 
[6] proposed a negotiation mechanism in which an agent can 
adapt itself by changing its preferences and behavior using 
dynamic models. This learning model is applied by using 
Genetic Algorithm (GA) in process of decision making, so that 
agent can gradually acquire appropriate negotiation knowledge 
based on its negotiation history with that opponent. Thus, 
Raymond negotiation model aims to reach the same property 
but by using machine learning techniques. 

B. Intellectuality 

As intellectual capability is one of the substantial 
characteristics in agent technology, learning appears as one of 
the most important and effective methods in improving 
negotiation agents. Recently, many researchers enriched their 
negotiation agents and models by using different kinds of 
learning algorithms or machine learning techniques [6, 9, 26, 
29-32]. Former attempts in this area were applied by using 
some sort of machine learning techniques (e.g. GA, fuzzy, re- 
enforcement learning, simulated annealing and data mining), 
distribution probabilistic analysis or by applying some pre- 



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programmed strategies. In Intelligent Trading Agents(ITA) 
model [9] four different types of strategies were introduced to 
help agent's to choose the next action to be taken. E- 
Commerce Negotiation (E_CN) [10] also presents complex 
negotiation tactics combined with a sort of distribution 
probabilistic analysis to predict the opponent's type which will 
end to choose the best possible strategy for the next round. 
Other researchers tried to employ machine learning techniques 
directly to the agent's decision making engine. For example, 
Raymond [6] applied a genetic algorithm on the space of 
possible offer for agent A to propose to agent B. Fig. 1 
represents an example of how agent offer encodes to the 
chromosomes and draws a tow-point crossover operation 
according to Raymond Lau[6]. 



Double Crossover 



Parent Offer 1 



Parent Offer2 



Child Offer 



1 

e 




20-40 




10-20 








10-30 



Quality 



Price Quality 

Crossover 



10-20 

Quality 




L> 10-30 



Figure 1. Encoding Candidate Offers [6] 

In this scenario agent A will generate a random pool of 
possible offer to propose to agent B. Then GA is applied on 
mating pool which is using three standard genetic operators: 
cloning, crossover, mutation. The outcome is a list of 
generated possible offers which are ranked by their utility 
values. The first offer in the list is the offer to be proposed to 
agent B by agent A. Proposed GA-based negotiation agents 
assist agents by learning their opponent's preferences. This 
improves the speed of the search in finding the best possible 
offer for both agents. Although this learning ability increases 
the overall utility rate to 10.3%, still there are some open issues 
to increase the performance of the system. One of the open 
issues is to acquire knowledge extracted from the recorded 
history or a trusted third party in order to setup the GA 
initialization accurately (e.g. possible range of value for genes). 

Guo et al. [32] suggested an algorithm to extract knowledge 
from a user and then inject it into the solution population. 
Then, a simulated annealing was applied to render the solution 
to make sure that the best possible offer will be proposed. 

Isabel et al. [26] proposed a multi-agent market simulator. 
In this market, agents have the possibility to negotiate through 
the pool which is regulated by a market operator (market 
administrator). Three data mining techniques are proposed to 
mine the administrator's transaction history, which contains all 
previous interactions and transactions among agents. The 



Vol. 8, No. 3, 2010 
Tow-Step clustering algorithm is used to cluster buyers 

according to agent's characteristics. After that, a rule-based 

modeling technique, C5.0 classification algorithm [26] is used 

to extract the consumption pattern of each cluster population. 

Finally, Apriory algorithm [26] is used to discover association 

between buyer details and purchases. A future direction for 

this research is to employ some sense of artificial intelligence 

to the seller agent which will result in a higher overall 

performance of the system. 



C. Applied Strategies 

The outcome of negotiation depends on several parameters 
including agent's strategy. Strategies are divided into three 
groups: (1) strategies which depend on time called time- 
dependent strategies, (2) strategies which depend on agent's 
behavior, called behavior-dependent strategies and (3) 
strategies which depend on how a specific resource is 
consumed. Recently there have been huge amounts of research 
on agent's strategies [9, 10, 26, 27, 29]. 

In IT A model [9], some time-dependent strategies were 
proposed, namely Desperate, Patient, Optimized patient and 
strategy Manipulation. Desperate strategy accepts the first 
acceptable offer that is suitable. In this strategy agent aims to 
reach a deal as soon as possible. Patient strategy waits until all 
negotiation threads reach a deal and then choose the best offer. 
This strategy guarantees the best possible deal but does not 
consider time constraint which is one of the most important 
factors in real market places. In Optimized patient strategy, the 
outcome of one sub -negotiation affects the performance of 
other sub-negotiations. Manipulation is a combination of 
above-mentioned strategies. A drawback of such strategies is 
the lack of adaptability as they are preprogrammed. 

E-CN model [10] used time-dependent strategies called 
Conceder, Linear and Tough. Conceder strategy means that an 
agent quickly lowers its preference values until it reaches its 
minimum reserved value. Linear strategy is when agent drops 
its reserved values but in a gradual manner. Tough strategy 
deals in a tough manner, meaning that it keeps its values until 
agent is close to deadline then suddenly drops the values to its 
reserved values. However, these sorts of strategies are sub- 
optimal in which using a sense of learning can improve the 
efficiency and robustness of system. 

Isabel et al. [26] proposed two types of behavior-dependent 
strategies named Composed Goal Directed (CGD) and 
Adaptive Derivate Following (ADF). CGD is based on two 
objectives which should follow sequentially. The first objective 
is to be sure that all needed goods are sold or purchased. The 
second tries to reduce the pay off of the deal or increase the 
benefit. ADF is based on the revenue earned in the previous 
period as a result of changes in the price. If the change of price 
by the last period produced more revenue than the previous 
period, then the strategy takes a similar action otherwise it will 
take a counter action. 

Although, time-dependent strategies seem simple, with 
concern to the time dependent of negotiation agent, they have a 
significant effect on system's outcome. A combination of 
alternatives of different types of tactics were proposed in this 
area [16, 23]. 



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D. Gathering Information 

The lack of information about environment and opponent 
agents is one of the major issues on negotiation systems [18, 
26, 32-35] since these information assist the agents to choose 
suitable agents and strategies. Such information can be 
gathered via agent's recorded negotiation histories or via 
trusted third party agents and referral systems. The referral 
mechanism allows agents to find their required resources if 
there is any agent with the required expertise close to the 
location of the neighboring agents [35]. Many researchers [18, 
26, 32-36] have focused on this issue with the aim to improve 
their negotiation agent models. Guo [32] in his proposed 
algorithm referred to information as an important factor which 
assists suggested model in learning multi- attribute utility 
function. Gathering information is mentioned as the second 
step of the Guo algorithm Fig. 2. 



Utility Elicitation Algorithm 



Applying Evolutionary Operations 

Selection, Crossover and Mutation 



(IJCSIS) International lournal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
In that second step called "applying external knowledge", 

knowledge gathered from outside is obtained directly from user 

input or derived from user observation. The base solution is 

now modified by injecting external knowledge into the solution 

population. The knowledge will be accepted based on 

correctness of the knowledge and a certain probability [32] . 

Guo et al. [32] showed that taking this knowledge into account 

while generating solution population, has a significant effect on 

learning user preferences in multi- attribute negotiation. 



Applying External Knowledge 

Knowledge Acquisition and Integration 



Apply Local Search 

Local Refinment with Simulated Anealing 




Choo et al. [33] also presented a form of optimizing 
negotiation agents by employing genetic algorithms. This 
model attempts to learn the agent opponent's preferences 
according to the history of the counter offers based upon the 
stochastic approximation. One of the further discussions in the 
agent models is employing trust on negotiation agents 

Trust can help agents to follow their aims by gathering 
useful information about their opponent's preferences and their 
attitudes toward their goals. Multi Dimensional Trust (MDT) 
introduced by Griffiths et al [37] opened a future direction in 
the area of negotiation agent for finding trustworthy opponent. 
Griffiths et al [37] applied a weighting factor concept which 
enables agents to combine decision factors according to agents 
current preferences. Later on, Gujral [38] provided its agents 
with a model of recognizing the best trusted third party to 
obtaining information from. In that model, agents should 
consider the trustworthiness of the potential opponents in order 
to maximize the agent's rewards, inasmuch as the more reliable 
the trusty agent is, the higher the chance of reaching agreement. 

There are some other important factors in multi agent 
negotiation systems which, taken into account, can affect the 
outcome of negotiation's systems. These include the number 
and type of issues considered in agent services [16, 39], agent 
attitude [17], one-sided or two-sided commitment [11, 40], 
bilateral [39] or multilateral [6] negotiation. 

The standout research and relative optimization techniques and 
methods of negotiation agent models are summarized in table 1 
below. 



Figure 2. Utility elicitation algorithm[32] 



TABLE I. 



Popular Techniques and methods for optimizing agent negotiation systems 



Optimization Methods Te chnique s 



Exempular description 



Stand out Researches 



Gathering information 



Referral Systems 

Trust 

History 



Asking information from neighbors 
Based on trust worthy of opponent 
based on recorded experienced 



Ebadiet.al. (2008) 

Griffiths (2005 ) ,Gujral et. aL (2007) 

Choo et.aL(2009), Guo et.aL(2003) 



A daptability 



adaptive learning techniques 
Flexible models 
Dynamic models 



learnin adaptive factor during trading 
overcoming pre-programmed tactics 
Dynamic methods and prograrnming 



Raymond (2009),Magda et.al.(2009) 
Zang et.al(2007),chung-wei(2008) 
Raymond (2009) 



Inte lie ctuality 



Marc nine learning techniques: 



Data mining techniques 
Others 



Genetic Algorithm 

Bayesian Learning 

Reinforcement Learning 

Fuzzy Logic 

Evolutionary Learning 

Apriory algo/Clas s if ic ation(C5) Algo 

Simulated Annealing 



Choo et.aL(2009), Magda et.al.(2009) 

Duong e.aL(2004),Choo et.al(2009) 

Sen et.aL(2007) 

Cheng et.al.(2005) 

Raymond(2009) 

Isabel et.al.(2008) 

Isabel et.aL(2008) 



Applied Strategies 



Time dependant tactics 



imitative tactics 

Resource dependant tactics 



B oulware/Conc eder/Line ar 

Relative tit-for -tat/Random absolut tit- 

for-tat/Averaged tit-for-tat 

Dynamic deadline /Re source estimation 



Iyad et.aL(2002),Doung et.al.(2004) 



Isabel et.al.(2008) 
not common 



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V. APPLICATIONS 



After specifying the domain of classification and 
optimization of negotiation agents, some outstanding works 
carried out during recent years will be reviewed. Following the 
new generation of the models we will track the progressing 
flow of agent generations. In addition, the whole negotiation 
systems proposed in the models will be analyzed to elaborate 
on advantages and drawbacks of these systems. These systems 
are analyzed in terms of desirable negotiation protocol, 
negotiation strategy, agent characteristics and negotiation 
setting. 

1) Desirable negotiation protocol represents the rules that 
govern the interaction between negotiators [22]. According to 
Nguyen and Jennings. [41], desirable properties for a 
negotiation protocol include pareto efficiency and Guarantee of 
success. 

2) Negotiation strategy is the arrangement of a series of 
actions that the agents plan to take through the negotiation. 
Negotiation tactics specify whether it is time dependent, 
resource dependant or imitative. In some cases, negotiation 
tactics are a combination of aforementioned tactics proposed by 
models. 

3) Agent characteristics specify agent's knowledge and 
experience, learning and adaptation capabilities. 

4) Negotiation setting deals with factors which are related 
to problem domain. It includes number of negotiation issues 
and number of parties involved. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
desperate, patient and optimal patient. Optimal patient helps 

agents to assist agent's autonomous behavior in dynamic 

environments. In this case, preferences of agents will be 

marked by weighting factor which represents the degree of 

importance of every issue. 



Weighting factor was firstly introduced by Griffiths [37]. 
Later on, this concept under the term of Multi-dimensionality 
[38] was used by many agent researchers [43, 44] to evaluate 
their standard measurements and finding the more appropriate 
deal or opponent. However, ITA's strategies help agent to gain 
higher performance but still the act of agent is bounded by 
premier choice of strategies before each round of negotiation. 
This will reduce the adaptability of negotiation agent. 



I 


)ynamic Environmt 


;nt 
















/ Buyer agent ) 




















Sub-buyer 1 






Seller 1 
















Coordinator &-► 


Sub-buyer 2 






Seller 2 








! 










Sub-buyer n 






Seller n 

























The analysis will deal with the evolution that the primary 
Kasbah model [7] underwent from 1996 to 2009, leading to 
GAMA model (table2) 

Kasbah model [7] is a simple one-to-one negotiation 
framework. Its application is on e-commerce where the agent 
technology will meet the web-based systems and try to 
overcome the need of online trading by applying some 
autonomy on trading. Kasbah model is single issue and 
considers price as the most important issue in negotiation. 
Therefore, agents are searching to make a deal with appropriate 
price even before they meet their deadline. Lack of adaptation 
and intellectuality is obvious draw-back of Kasbah' s 
negotiation agents which is overcome by defining new versions 
of Intelligent Trading Agents such as ITA and e-CN. 

IT A [9] is a one-to-many negotiation framework which is 
an improved version of negotiation systems in terms of number 
of issues considered in negotiation and in terms of 
communicating as it follows bilateral negotiation. In bilateral 
negotiation, agents have the ability to send offer and counter- 
offer in both ways. ITA presents new system architecture 
represented in Fig. 3. 

ITA buyer agent includes two components, namely 
coordinator and sub-buyers. Buyer agent will establish several 
one-to-one negotiations between sub-buyers of buyer agent and 
sellers. In this work, agent preferences are represented as a 
constraint satisfaction problem as described by Vipin [42]. 
Moreover, this model proposes different sort of strategies like 



Figure 3. System architecture adapted from ITA system [9] 



The new version of ITA called e-CN was proposed by 
Nguen and Jennings [10]. This method uses several number of 
agent services' issues considered in negotiation. Also, a factor 
is introduced as "probability of agent distribution" which 
represents the probability of allocating types of agent in the 
environment. Nguyen [10] believes that agents can be divided 
by their behavior into two groups of agents called Conceder 
and Non-conceder. Conceder stands for agents who are willing 
to concede in order to reach a deal while Non-conceder stands 
for agents who are not willing to sell, otherwise there is a 
special amount of benefit for them, so they act in a tough 
manner. 

In e-CN model, agents choose their strategies based on a 
method of predicting the expected utility of chosen strategy 
considering the current situation. This Expected utility will be 
evaluated according to 3 important probability factors: 
probability of agent's distribution, possibility of reaching 
agreement and average utility value if agent reaches the 
agreement. 

The disadvantage of proposed concession strategies in e- 
CN and ITA is that in every round of offering new proposal, 
agents are conceding while there is a possibility to find a 
mutually acceptable proposal with the same utility level but 
different values of the issues. Future research should be done 



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to investigate a sort of similarity function that reveals the 
negotiable issues with higher importance. Finding important 
negotiable issues assists agents to reach to an agreement using 
the slightest possible amendments. We believe this will result 
in higher optimization and also faster deals in terms of time 
consuming. Aforementioned future developments will result in 
decreasing negotiation cost. 



Although these models support bilateral multiple 
concurrent negotiation, there is still much more need to assist 
the agent decision making methods. Helping agent to predict 
its opponent next action or finding the opponent preferred 
issues is another future work. As we discussed in section IV 
this objective could be achieved by enhancing the 
intellectuality of agents. Referring to table 1 there are many 
possible techniques to investigate as future studies. An 
appropriate learning technique could lead this model to find the 
pareto optimal solution. 

Cheng [23] proposed a heuristic, multiple issue, one-to- 
many model for negotiations in a third-party-driven e-market 
place. These negotiation agents employ trade-off tactics using 
fuzzy inference systems to generate new offer in each round. 
Trade-off tactics are navigated from time-dependent and 
imitative tactics. 

Although, Cheng's proposed model is pareto-efficient and 
highly adaptive revealing the importance level of issues to the 
other agents, it violates the privacy of information. These sorts 
of assumptions are highly inappropriate since they are hardly 
acceptable in the real world environments such as e-markets. 

A negotiation meta strategy for one-to-one bilateral 
negotiation was proposed by Ros and Sierra[16]. Meta strategy 
is a combinatorial sequence of concession and trade-off tactics 
which will try to keep the aspiration level, otherwise there is no 
possible offer by the current aspiration level. Combining 
tactics allows agents to outperform better in different situation 
which fulfills the adaptive capability of agents. In addition, 
detection of opponent agent preferences helps agents to 
propose mutually acceptable offers. As a consequence, the 
system's final success rate increases. 

Although this model is placed in a satisfactory level of 
adaptability, it is recommended that further research be 
undertaken in order to learn the opponent agent's type. Finding 
the correct classification for type of agent could increase the 
chance of reaching agreement. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
Raymond [22] performed a similar series of experiments in 

terms of models characteristics. Raymond negotiation agents 
are enhanced as they are promising in supporting real-world e- 
market places environment. This application sustains multi- 
party, multi-issue, many-to-many negotiation which are based 
on parallel and distributed decision making model. Moreover, 
Raymond [22] introduced his novel genetic algorithm in this 
experiment. The final result of the experiment showed that an 
evolutionary negotiation agent guarantees pareto optimal 
solutions underneath dynamic negotiation situation, for 
example in the incidence of time limitations. 



However, as we mentioned in section IV D, embedding a 
sort of strategies could enhance the success rate of the two 
aforementioned negotiation systems. 

Magda [45] introduced an agent mediated shopping system 
called Genetic Algorithm driven Multi- Agents (GAM A). 
GAMA is a multi issue bilateral negotiation system enhanced 
by learning ability. GAMA studies the effect of participating 
opponent agent's preferences in decision making of agents. In 
order to do that, one of the offers from the opponent agent's 
previous offers (or list of offers) is chosen as one of the parents 
and the other parent is chosen from agent's own preferable 
proposals. Then, the mutated offspring is generated. The new 
generated offspring is a potential satisfactory offer as it is 
mutually acceptable for both agents. Experimental results 
demonstrated that GAMA achieved to a higher satisfaction rate 
while reaching to the higher numbers of the deal in comparison 
with traditional GA methods. 

One of the advantages of this work considering these parent 
selections is increasing the adaptability of the system, since 
every change of opponent agent's preferences effects the 
decision making of agents. In addition, proposing mutually 
admissible offer causes to reach an agreement in fewer rounds 
of negotiation, thus reducing the cost of negotiation. In future 
investigations, it might be possible to experiment this model 
under qualitative issues as an alternative of quantitative issues. 
This could result in higher optimization and also faster deals in 
terms of time consumption. 

In order to have a better understanding on the overview of 
applications discussed, table2 illustrates the general 
characteristics of the applications discussed. 



Choo et al. [19] conducted a research on one-to-many 
bilateral negotiation with multiple issues (quantitative issues). 
The system architecture of the study was based on ITA's 
system architecture. They investigated two different machine 
learning approaches genetic algorithm and Bayesian learning 
called GA improved-ITA and Bayesian improved-ITA. The 
result obtained from the final analysis showed that GA- 
improved-ITA outperforms, Bayesian-improved-ITA in 
maximizing the joint utility and negotiation payoff at the same 
time as it increases the negotiation cost. 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 

TABLE 2. A COMPREHENSIVE ANALYSIS over system application progress during the last thirteen years. 



Negotiation system characteristics 


Kasbah(1996) 


ITA(2001) 


e-CN(2004) 


Cheng et.al.(2005)Ros et.al.(2006) 


Choo et.al.(2009) 


Raymond(2009) 


GAMA(2009) 


Cardinality of Negotiation Domain 


single-issue 


multiple-issue 


multiple-issue 


multiple-issue 


multiple-issue 


multiple-issue 


multiple-issue 


multiple-issue 


Cardinality of Communication 


unknown 


bilateral 


unknown 


bilateral 


bilateral 


bilateral 


mutilateral 


bilateral 


Number of agents involved in negotiaioi 


one-to-one 


one-to-many 


one-to-many 


one-to-many 


one-to-one 


one-to-many 


many-to-many 


one-to-many 


Qualitative negotiation value of issues 


- 


- 


V 


V 


V 


- 


V 


- 


Quantitative negotiation value of is sues 


V 


V 


V 


V 


V 


V 


V 


V 


Privacy of model 


V 


V 


4 


V 


V 


V 


V 


V 


Privacy of information 


V 


V 


V 


not-private, see Des 


V 


V 


V 


V 


Negotiation tactics includes: 


















TIme/REsource dependant or IMitative 


- 


Time dependant 


mix(TMM) 


Trade-off(TI+IM) 


Mix(TMM) 


Time dependant 


Time dependant 


- 


Intellectuality 


- 


- 


Bayesian learning 


Fuzzy inference 


- 


Genetic Algorithm, 
Bayesian learning 


Genetic 
Algorithm 


Genetic 
Algorithm 


Pare to efficiency 


- 


- 


- 


Preto Optimal 


- 


- 


Preto Optimal 


- 


Guarantee success 


- 


V 


V 


- 


- 


- 


- 


- 


Type of agents involved 


unknown 


unknown 


conceder 
non-conceder 


NegoTo, Random, 
alternate, 
TOAgent, 
NegoAgent 


unknown 


unknown 


unknown 


unknown 


Adaptive ability 


non-adaptive 


non-adaptive 


semi-adaptive 


adaptive 


adaptive 


non-adaptive 


adaptive 


adaptive 



This table shows that embedding strategies in negotiation 
agent models can increase the final system outcome. This 
enhancement could be in terms of increasing adaptability or 
assuring the guarantee of success. As illustrated, most of the 
models empowered by machine learning techniques are fully 
adaptive. As discussed in section IV A, adaptability is an 
important characteristic for negotiation system embedded in 
open environment. Also, as we can see in e-CN and Cheng et 
at. models [10, 23] an accurate classification on agent's type is 
driving the negotiation model to a desirable level of 
adaptability. 

In order to reach to the pareto optimal result, agents most be 
equipped with an effective learning method or a suitable 
strategy. These cases show that learning ability helps agents to 
predict opponent's characteristics (e.g. preferences, reservation 
value, attitude and type) accurately. Therefore, by choosing 
the best possible strategy (action) we can reach to a pareto 
optimal result. 

Every negotiation mechanism is desirable to meet some 
important requirements. These include pareto efficiency and 
guarantee of success. The power of applying suitable strategy 
is revealed by assuring the guarantee of success only by 
employing the appropriate strategy. 

We believe tables 1 and 2 provide a good resource for 
future developments. Since table 2 highlights the shortcomings 
and loopholes of the above-mentioned models (represented by 
"-" or "non"), developers can easily investigate future studies 
in order to overcome these gaps by the help of the optimization 
methods introduced in table 1. 

As this study shows, artificial intelligence is an important 
characteristic of multi agent systems which has been used as a 
popular enhancing technique to optimize the final outcome of 
many negotiation agent based systems. As Jennings [46] 
admits" undoubtedly the main contributor to the field of 
autonomous agents is artificial intelligence". 



Artificial intelligence techniques such as GA [6, 19, 32] , 
fuzzy logic [23, 25], simulated annealing [32] ,neural network 
[47, 48] and re-enforcement learning [30] have been used 
broadly to improve agent's intellectuality in recent years. 
According to our review, GA is the most popular learning 
technique among others as usually models that employ GA in 
negotiation systems reach to a higher final utility than other 
techniques (e.g. [22, 49]) as shown in Fig. 4. 




Figure 4. Comparison of final utility value of different machine learning 
methods 

This is due to the pattern of common problems defined in 
negotiation which match GA technique characteristics, since, in 
typical negotiation problems, we face a big space of feasible 
solution and the goal is to find the best solution. Furthermore, 
specifying the fitness function for population ranking is a 
straightforward task as generally it equals to utility function for 
evaluating offers. 



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VI. CONCLUSION 

In the last few years we have witnessed an incredible 
growth of multi agent systems on e-commerce. One of the 
most important driving forces behind MAS research and 
development is the internet. By increasing the human demand 
on online trading, negotiation has become one of the most 
important topics in MAS. In this area, many researchers tried 
to improve the performance of the negotiation agent's model. 
This is achieved by using adaptive methods, employing 
learning abilities, applying strategic reactions and gathering 
information. To sum up, negotiation agents can act more 
efficiently when they are empowered with effective methods 
for gathering information which assists the agents in employing 
strategic actions to reach their goals. In addition, agents must 
be adaptive by changing their attitudes and learning their 
opponents' characteristics and preferences to improve the 
overall performance of the system. 

In order to improve agent negotiation systems, we need to 
understand the dimensions and range of options in these areas. 
To set up the foundation, we have developed a classification 
scheme which is specially aimed at negotiation agent systems 
on e-commerce. Negotiation agents can be categorized into 
different groups with respect to (1) number of agents involved 
in negotiation, (2) type of agents involved in negotiation, and 
(3) according to negotiation agent attitude involve in 
negotiation. 

This classification system was demonstrated on an assorted 
range of outstanding negotiation model and the outcome is 
summarized in table 2. The purpose of this classification was 
to present a complete and systematic source to objectively 
compare and contrast different negotiation models. Such a 
classification method is essential for developers as it supplies a 
resource of differentiating competing alternatives for the area 
of negotiation agent's models to exploit. 



VII. 



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AUTHORS PROFILE 



Sahar Ebadi is currently doing her Master degree in 
Faculty of Computer Science and Information 
Technology, UPM. Sahar has received her B.Sc in 
software computer engineering field in 2006 from 
Iran Azad University. Her research interest includes 
Artificial Intelligence and Autonomous Agents and 
Data mining. 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 



Customized Digital Road Map Building using 

Floating Car GPS Data 



G. Rajendran 

Assistant Professor of Computer Science, 

Thiruvalluvar Government Arts College, 

Rasipuram-6 37401, Tamilnadu, India 

guru. rajendran @ yahoo.com 



Dr. M. Arthanari 

Director, 

Bharathidasan School of Computer Applications, 

Ellispettai-638116, Tamilnadu, India 

arthanarims vc @ gmail . com 



M. Sivakumar 

Doctoral Research Scholar, 

Anna University, Coimbatore, Tamilnadu, India 

sivala @ gmail . com 



Abstract — The vehicle tracking, navigation and road guidance 
applications are becoming more popular but the presently 
available digital maps are not suitable for many such 
applications. Among the drawbacks are the insufficient accuracy 
of road geometry and the delayed time in loading the unwanted 
data. Most of the commercial applications in vehicle tracking 
require digital maps which have only roads and places of interest 
whereas the currently available maps show all available roads 
and places. A simplified map building process to construct 
customized high-precision digital maps from floating car data 
obtained from Global Positioning System (GPS) receivers is 
presented in this paper. The data collected from the GPS receiver 
fixed in a moving car are used to construct the customized digital 
road maps. The approach consists of six successive steps: 
Collecting floating car data (FCD) for desired road segments in a 
log file; refining the log file; constructing the road segments using 
the data present in refined log file; merging the segments which 
has negligible slope; refining the road intersections; and labeling 
the points of interest. The quality of outcome of the map making 
process is demonstrated by experimental results and the results 
indicate that customized road maps of required routes with good 
accuracy can be built with the help of the proposed map making 
process. 

Keywords- digital map; global positioning system; floating car 
data; road network 

I. Introduction 

Map is a total or partial depiction of the structure of the 
earth (or sky) on a plane, such that each point on the map 
corresponds to an actual point on the earth (or in the sky). 
Digital maps are used to produce this depiction in electronic 
form. The digital maps are usually represented as graphs, 
where the nodes represent intersections and the edges are 
unbroken road segments that connect the intersections [1]. 
Each segment has a unique identifier and additional associated 
attributes, such as the number of shape points that approximate 
its geometry roughly, the road type (e.g., national highway, 
state highway, city road, street etc.), name, speed information, 
and the like. Digital map building is a process that utilizes the 
information supplied by external equipments in terms of 



physical features of an environment [2, 3, 4]. This information 
is taken from different positions along the path followed by a 
moving object. Once the map is obtained, it can be used to 
improve the quality of the paths, to locate a target, to help 
object recognition, to define expectations in the trajectory or to 
replay the travelled path of a moving object. 

Digital maps of required roads with good accuracy are 
needed in a number of commercial applications. But the 
presently available digital maps are fully populated with dense 
roads and other information, most of which are irrelevant to the 
requirement. Because of the presence of these unwanted data, 
the loading time of the map in computer memory is also more 
which results in slower execution of the application. Hence the 
need for building customized digital road maps is essential and 
such a map building process will also eliminate the expenses 
involved in buying digital maps. This paper presents a 
customized digital road map building process which can be 
used to construct high quality maps of desired roads and 
locations. The source code for the intermediate processing 
steps is written in Matlab 7.6. 

Map building process has already been discussed by some 
of the researchers, but with limitations like complexity in map 
building and insufficient accuracy. These limitations have been 
addressed in this work. The remainder of this paper is 
organised as follows. Section 2 of this paper describes road 
network model and the collection of floating car GPS data. 
Related work in this area is discussed in Section 3. In Section 
4, the map making process is discussed. The experimental 
results are dealt in Section 5. The work is concluded and the 
possible improvements are discussed in Section 6. 

II. The Road Network Model and the Floating Car 
Data 

A. The Road Network Model 

The road network data are the basis for vehicle tracking and 
related applications. The road network model is represented 
with two-dimensional line segments. 



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The road network model [5] is represented by the equation 

R n = (N,S), 

N={n I n=(x, y), x, y G Coordinates}, 

S= {s I s = <m, n>, m, n G N}, 

wherein R„ represents the road network, N represents the node 
set that indicates the coordinate point set of the road in the road 
network which is a pair of longitude and latitude (x,y), S 
represents the road segment set of the road network which is 
composed by the sequence <m, n>. S represents one directional 
road that has the beginning node m=begin(s) and the 
termination node n=end(s). Fig. 1 shows a road network with 
nodes, segments and an intersection point. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 
The Floating Car (Probe Car) technique is one of the key 
technologies adopted by the ITS to get the traffic information 
in recent years [9]. Its basic principle is to periodically record 
the location, direction, date, time and speed information of the 
traveling vehicle from a moving vehicle with the data of the 
Global Position System (GPS) as shown in Fig 2. The 
information can be processed by the related computing model 
and algorithm so that the floating car data can be associated 
with the city road in real time [10]. This data can also be used 
as a source of data for creating research and commercial 
applications on vehicle tracking and road guidance systems. 



Ruad I liters sciwri 



node 1 




segment 1 segment 2 

node 2 



node Z 



node 4 
Segment 3 segment 4 

Figure 1. Basic elements of the road network model. 

If there is a road segment sequence <Sj, Sj,..., s k > in the 
network R n =(N, S), the termination node of every road segment 
is the beginning node of the next road segment, this sequence is 
called one directional road of the road network. 

B. Floating Car Data 

Nowadays, the main research focus in the community of 
Intelligent Transport Systems (ITS) is how to acquire real-time 
and dynamic transportation information. This information can 
be applied in the transportation area like vehicle tracking, 
navigation, road guidance and so on. GPS is one of the system 
which is used to provide real time information on moving 
objects. It is a Satellite Navigation System which is funded and 
controlled by the U. S. Department of Defense [6, 7]. The 
system consists of three segments viz., satellites that transmit 
the position information, the ground stations that are used to 
control the satellites and update the information, and finally 
there is the GPS receiver that computes its location anywhere 
in the world based on information it gets from the satellites [8]. 



GPS Satellites 





Figure 2. A moving vehicle (Floating car) fixed with a GPS receiver 



III. Related Work 

The history of map making process starts with the 
Egyptians who for the first time constructed a map for revenue 
collection three thousand years ago. The digital map building is 
a new concept developed after the revolution in Information 
Technology. Though some work has been done in this area, a 
number of map building techniques are being proposed to suit 
the emerging requirements. 

Y.L. Ip et al., have presented a technique for on-line 
segment-based map building in an unknown indoor 
environment from sonar sensor observations [4]. In their 
approach, the environment data is first obtained from a rotating 
sonar sensor, analyzed and fed to the Enhanced Adaptive 
Fuzzy Clustering (EAFC) module to extract the line segments 
within the workplace of robot. The basic motive is to use full 
data set to obtain an initial approximation cluster centers via 
Fuzzy c-mean. This initial approximation helps reducing the 
number of iterations required for Adaptive Fuzzy Clustering 
(AFC). This approach is somewhat similar to Fast Fuzzy 
Clustering (FFC) [11] which is a strategy to speed up the Fuzzy 
c-mean (FCM). In order to facilitate the map building, the 
workplace of the robot is divided into squared areas as cells in 
order to extract the line segments. This mechanism reduces the 
computation time when extracting the line segments within the 
world frame. EAFC uses the Noise Clustering (NC) technique 
proposed in [12] to extract the line segments. EAFC also uses 
adaptive fuzzy clustering algorithm [13] and fast fuzzy 
clustering [11]. These algorithms are combined into a single 
algorithm with enhanced characteristics such as improvement 
in the computational burden and reduction of the effect of noisy 
data in fuzzy clustering algorithm. Besides, the authors have 
proposed a compatible line segment merging technique to 
combine the similar line segments to a single long line segment 
as a mechanism to reduce the number of segments in the world 
model and further improve the quality of the map. This 
technique is applicable for constructing maps of indoor 
environment related to robotic applications. 

Stefan Schroedl et al., have contributed to map-building by 
introducing a system that generates digital road maps that are 
significantly precise and contain descriptions of lane structure, 
including the number of lanes and their locations, along with 
detailed intersection structure[l]. The authors combine a large 
quantity of possibly noisy data from GPS for a fleet of vehicles, 
as opposed to a small number of highly accurate points 
obtained from surveying methods. It is assumed that the input 
probe data is obtained from vehicles that go about their usual 



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business unrelated to the task of map construction, possibly 
generated for other applications based on positioning systems. 
The work of authors include the development of a spatial 
clustering algorithm for inferring the connectivity structure of 
the map from scratch, the development of a lane clustering 
algorithms that can handle lane splits and merges and forming 
an approach to inferring detailed intersection models. This 
system requires data from hundreds of vehicles already 
connected to tracking systems for constructing a single segment 
of the road. 

Thus a few number of digital map building techniques are 
available but they suffer from high complexity of map building 
process, requirement of more data and dependence on technical 
skills of the person who is working with map building. The 
map building process proposed in this work addresses these 
problems. The process discussed here is a very simple one and 
even a novice user without technical skills can easily create 
route maps according to his requirements. 

IV. Map Building Process 

A. Collecting Floating Car GPS data in a log file 

GPS receiver communication is defined with National 
Marine Electronics Association (NMEA) specification. The 
NMEA has developed a specification that defines the interface 
between various pieces of marine electronic equipments. The 
NMEA standard permits marine electronics to send information 
to computers and to other marine equipments [14] in 
predefined formats. Most computer programs that provide real 
time position information recognize data that are in NMEA 
format which includes the complete latitude, longitude, 
velocity and time computed by the GPS receiver. In NMEA 
specification system, the collected GPS data is converted into a 
line of text, called a sentence, which is totally self contained 
and independent from other sentences. The commas act as 
terminators for the sentences and the programs that read the 
data should only use the commas to determine the end of a data 
item. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 
receiver module was fixed in a moving car and the generated 
NMEA sentences are stored in a log file in a laptop kept in the 
moving car. This GPS receiver generates $GPGGA, $GPGSA, 
$GPRMC, $GPVTG and $GPGSV sentences at regular time 
interval of one second. A list of NMEA sentences produced by 
the GPS receiver and stored in a log file when travelled in a 
road is given in Fig. 3. 



3PGGA,1 12537.613,1 123.5703.N.07739.6095,E,1 ,10,00.8,191 .4,M.-92.6,M,.MF 

3PGSA,A,3,03,06,07,11,13.16,19,20 1 23,32,„1. 8,0.8,1 .6*39 

3PRMC1 12537.613.A,1123.5703.N.07739.6095,E, 12.58, 193.19,240410, „A*57 

3PVTG f 183.19 I T„ l 12.58,N,23.29 I K l A*77 

3PGGA.1 12538.61 3.1 123.5664.N.07739.6085,E,1 ,10,00.8,1 91 .4.M.-92.6,M M *41 

3PRMC, 11 2538.61 3.A,1123.5664.N.07739.6085,E, 14.67,1 93.98,240410, „A*5A 

3PVTG,193.98.T,.,14.67,N,27.18,K.A*72 

3PGGA,1 12539.613.1 123.5625.N.07739.6074,E, 1,10,00.8, 191. 5, M.-92.6.M,.*4A 

3PGSV,3,1, 10,3,43,1 18.45,6,35.099,45,7, 16,285,34.1 1 ,12,204,41 *4E 

3PGSV,3,2,10,13,20,332,38,16,31,031,40.19,30,151,44.20,62.248,42V3 

3PGSV,3,3,10,23,40,350,42,32,59,185,46 *7E 

3PRMC,112539.613.A,1123.5625.N.07739.6074,E,14.41,195.11,240410,„A*53 
3PVTG,195.11.T,., 14.41, N,26.70,K.A*7E 

3PGGA.112540.613.1123.5584.N.07739.6062,E,1, 10,00.8,191 .7,M.-92.6,M,.*49 
3PGSA,A,3,03,06,07,11,13.16,19,20,23,32,„1.8,0.8,1.6*39 
3PRMC,112540.613.A,1123.5584.N.07739.6062,E,15.43,196.10,240410,„A*53 
3PVTG,196.10.T,.,15.43.N.28.57,K.A*74 

3PGGA,112541.613,1123.5539.N.07739.6047,E,1, 10,00.8,191 .8,M.-92.6,M,.M6 
3PRMC1 12541 .613.A,1123.5539.N.07739.6047,E,17.12,197.57,240410,„A*57 
3PVTG,197.57.T„,17.12.N,31.70.K.A*7D 

Figure 3. Log file of floating car GPS data with $GPGG A, $GPGS A, 
$GPRMC, $GPVTG and $GPGSV sentences 

In order to collect the floating car GPS data, Wonde-X 
series GPS receiver (ZX4125) module was used. This GPS 



B. Refining the log file to get $GPRMC sentences 

The log file contains a number of different types of 
sentences but the recommended minimum sentence C, 
$GPRMC, provides the essential GPS PVT (Position, Velocity 
and Time) data. This data is used to locate moving objects in 
terms of latitude and longitude. The $GPRMC data format is 
given in Table I. The moving object, if attached with a GPS 
receiver, can be located with the help of this NMEA sentence. 



TABLE I. 



$ G P R M C DATA FORMAT 



Data Item 


Format 


Description 


Message ID 


$GPRMC 


RMC protocol header. 


UTC Time 
(Coordinated 
Universal Time) 


hhmmss.sss 


Fix time to 1ms accuracy. 


Status 


Char 


A Data Valid. 
V Data invalid. 


Latitude 


Float 


Degrees * 100 + minutes. 


N/S Indicator 


Char 


N=north or S= south. 


Longitude 


Float 


Degrees * 100 + minutes. 


E/W Indicator 


Char 


E=East or W=West. 


Speed over Ground 


Float 


Speed Over Ground in knots 


Course over Ground 


Float 


Course Over Ground in 
Degrees 


Date 


ddmmyy 


Current Date 


Magnetic Variation 


Blank 


Not Used 


E/W Indicator 


Blank 


Not Used 


Mode 


Char 


A Autonomous 


Checksum 


*xx 


2 Digits 


Message 
Terminator 


<CRxLF> 


ASCII 13, ASCII 10 



An example of $GPRMC NMEA sentence is given below: 



$GPRMC, 120642.206, A, 1118.4253,N,07742.4325,E,3 1.6, 
317.52,140510„,A*62 

Where 

$GPRMC : Recommended Minimum sentence C 

120642.206 : Fix taken at 12:06:42.206 UTC 

A : Status A=active or V=Void. 

1118.4253,N : Latitude 11 deg 18.4253' N 
07742.4325,E : Longitude 77 deg 42.4325' E 

31.6 : Speed over the ground in knots 

3 17.52 : Course over the ground 



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140510 

A 

*62 



: Date -14th of May 2010 

: Autonomous mode 

: The checksum data, always begins with 



$GPRMC.112537.613,A,1123.5703,N,07739.6095.E, 12.58,193.19,240410. 
$GPRMC. 11 2538.61 3,A,1123.5664,N,07739.6085.E, 14.67,1 93.98,240410, 
$GPRMC.112539.613A1123.5625,N,07739.6074.E, 14.41, 195.1 1,240410, 
$GPRMC1 12540.613 A1123.5584,N,07739.6062.E, 15.43,196.10,240410. 
$GPRMC1 12541. 613A1123.5539,N,07739.6047.E,1 7. 12, 197.57,240410. 
$GPRMC1 12542.612 A1123.5489,N,07739.6032 i E,18.72,196.60,240410. 
$GPRMC11 2543.61 2A1123.5434,N,07739.6018.E,20.23,1 94.56,240410. 
$GPRMC1 12544.612 A1123.5380,N,07739.6004.E,20. 16,194.21, 240410. 
$GPRMC1 12545.612 A1123.5327,N,07739.5991.E,19.48,193.18,240410. 
$GPRMC.112546.611A1123.5276,N,07739.5979.E,18.70,193.55,240410. 
$GPRMC.112547.611A1123.5231,N,07739.5968.E,16.84,193.56,240410. 
$GPRMC.112548.611A1123.5192,N,07739.5958.E,14.45,193.67,240410, 
$GPRMC.112549.611A1123.5159,N,07739.5951.E.12. 11,192.77,240410, 
$GPRMC.112550.611A1123.5132,N,07739.5945.E,9.81, 191.68,240410,, 
$GPRMC.112551.610A1123.5113.N,07739.5941.E,7.23,191.44,240410, 
$GPRMC.112552.610A1123.5101,N,07739.5939.E,4.31, 192.94,240410, 
$GPRMC.112553.610A1123.5096,N,07739.5937.E,1. 85,193.33,240410, 
$GPRMC.112554.610A1123.5095,N,07739.5937.E,0.00,193.33,240410, 
$GPRMC.112555.610A1123.5095,N,07739.5937.E,0.00,193.33,240410, 
.GPRMC.112556.609A1123.5095,N,07739.5937.E,0.00,193.33,240410, 



Figure 4. Refined Log file of $GPRMC sentences 




Hence the next step in map making process is to refine the 
log file by removing other sentences in such a way that it 
contains the $GPRMC sentences only as shown in Fig 4. This 
refined log file now contains the path of the probe car in terms 
of latitude and longitude at an interval of one second per 
sentence. 

C. Segment Extraction 

The road segment extraction is done by considering the 
locations of the probe car at a fixed time interval. Since in most 
cases there is no significant distance between the probe car and 
the road centre line, it is assumed that the path of the vehicle is 
along the road centre line. The idea is to pick out 'k' locations in 
road centre line continuously by interleaving 'n' $GPRMC 
sentences. When this process is iterated, the i th location of the 
moving vehicle Xi,y (longitude Xj and latitude y) for all the 
values of T ranging from 1 to 'k' along the road centre line at 
'm' seconds interval is obtained. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 

After obtaining the points at road centre lines after 

interleaving m sentences in the middle, the slope between two 

'* adjacent points say x i+ i,y i+ i and Xi,y is calculated. Now an 

pmaa imaginary line perpendicular to the slope is drawn through Xj,yj 

and they are made to intersect an imaginary circle of radius 'r' 

on both sides as shown in Fig. 5. The points of intersection on 

either side (lxj, ly and rxj, ry) are recorded and they act as the 

nodes for the left line of the road and right line of the road, 

forming road segments. This process is repeated for all values 

of T ranging from 1 to k-1. It is to be noted that the radius of 

the imaginary circle determines the width of the road segment. 

Roads with different widths can be drawn by altering the radius 

'r' of the imaginary circle. 

The following algorithm is used to extract segments on 
either side of the road centre line. This algorithm extracts 
segments by computing road vectors on either side of the road 
and adds lines in vectors. The output of this algorithm for a 
sample data is given in Fig 5. 

Input : Refined log file; sentence count m; radius of 
imaginary circle r; number of sentences to be interleaved n. 




Output : Line segments on the left and right side of the road 
forming a road segment; line vector lx, ly for road left line and 
rx, ry for road right line. 

Step 1 : Initialize data items: m=0; i=0; n=40, r=0.5. 
Step 2: Repeat step 3 to step 4 till the end of log file. 
Step 3: Read the next sentence from the log file 
Step 4: Ifm>=n 

Read the sentence from the log file and 
store longitude into Xj and latitude into y. 
i=i+l 
m = l 
Else 

m = m+l 
:k = i-l 
: Repeat step 7 for i =1,2, . . . .,k-l 



Step 5 
Step 6 
Step 7 



dx=x i+1 -Xj 

dy=y + i -y 

slope_radians =tan 4 (dy/dx) 

6i =slope_radians / (7i/180) 

a= 6i + 90 

left_plot_radians = (n /180) * a 

lxi = Xj + r*cos(left_plot_radians) 

ly = y + r*sin(left_plot_radians) 

Plot(lXi, ly) 

p=a + 180 

right_plot_radians = (71/ 180) * P 

rxi = Xj + r*cos(right_plot_radians) 

ry = y + r*sin(right_plot_radians) 

Plot(rxi, ry) 
Step 8 : line(lx,ly) - Add the line in vectors lx and ly to the 
current axes. 

Step 9 : line(rx,ry) - Add the line in vectors rx and ry to the 
current axes. 
Step 10 : Stop. 



Figure 5. Obtaining Points for Left and Right Road lines using a Point of Road 

Centre line 



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aaj'i > \\«s«i(-|a|ra|Ba 




(IJCSIS) International Journal of Computer Science and Information Security, 
_ M?Z. 8, Afo. 3, 2010 

process is iterated for subsequent segments until all the 

segments are processed. 

The algorithm used for segment merging is given below. 

Input : Negligible slope (angle) X, road segments (vectors 
for left line lx,ly and right line rx,ry). 

Step 1 : Compute the slope(angle) of the line connecting 
Xi,yi and x i+ i,y i+ i of two adjacent segments (6j and i+ i) with 
respect to 'x' axis. 

Step 2 : Repeat step 3 till all the segments are processed 

(i=l,2,...,k). 

Step 3 : if abs(6i-6 i+ i) < X, merge the two segments i.e., 
remove intermediate node; otherwise do not merge. 



Figure 6. Road segments extracted on the left and right sides of road centre 
line (vehicle trajectory) 



D. Segment Merging 

The road segment merging technique is used to merge the 
similar basic road segments together to form a single road 
segment. It is observed from the outcome of the segment 
extraction algorithm that a number of adjacent road segments 
are similar in direction and they can be merged together to 
form a single segment. The primary advantage of segment 
merging is that it reduces the number of nodes in the road 
network and hence simplifies the map. It also results in faster 
generation of the map because of the less number of segments 
in the map. 




Figure 8. Road segments with left and right road lines after removal of vehicle 

trajectory 




Figure 7. Road segments extracted on the left and right sides of floating car 
GPS data after segment merging 

The criteria used to merge the road segments is the slope 
(angle) 6j of the line connecting Xi,y and x i+ i,y i+ i with respect 
to x axis. A threshold limit is set for the negligible slope 
(angle) X and if this slope(angle) is within the threshold, say 5° 
for two adjacent segments, then both the segments are merged 
together, otherwise the segments are left as they were. This 



Figure 9. Road Map after removing the plots for nodes 

Thus the segment merging algorithm is based on negligible 
slope and it merges the similar segments together which results 
in faster production of digital maps by eliminating unwanted 
nodes in the road network. The extracted segments are shown 



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ISSN 1947-5500 



in Fig. 6 and the merged segments are shown in Fig 7. It is 
observed that different threshold values for negligible slope 
(angle) X can be used to get required accuracy or width of the 
route map. The merged segments after removing vehicle 
trajectory are shown in Fig. 8. The final map of the road after 
removing the plots for nodes is shown in Fig. 9. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 
• Removal of a segment: Sometimes, a segment at the 
end of one road and a segment at the beginning of the 
next road may intersect and cross one another. In that 
case, one of the segments is deleted. 



E. Refining Intersection of Road Segments 

The obtained road network after segment merging is still 
unrefined at intersections of road segments. The refinement of 
road intersection is done in the following steps. 




Figure 10. Digital Map with unrefined intersection of road segments 













Da h * u 1 ^ %8ss a -| ai fl s 1 ■ a 




















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112) 
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1119 












1118.5 












?736 773:.: 7737 ^737 5 7738 


773E.£ 7^9 


7739.5 





Figure 11. Digital Map with refined intersection of road segments 

> Adding a segment: Many a times, there may be a 
necessity to add one segment either at the end or at the 
beginning of a road so that the road is connected to 
another road. In that case, a new segment is added. 
Sometimes, instead of adding an entire segment, only 
the left or right line of the road segment is extended. 



• Extending a segment: When two or more segments do 
not join at the intersection, the segments are extended 
based on the previous slope till they form refined 
intersection. It is to be noted that extending a segment 
is different from adding a segment. 

A map with unrefined road segments intersection is shown 
in Fig. 10. The road intersection is refined and Fig. 11 shows 
map with refined intersection of road segments. This process is 
repeated for entire set of road intersections till a fine-tuned map 
is obtained. 

F. Labeling Points of Interest 

A map contains points of interest which means places 
which are important and noted down on the maps. Placing text 
on a map is a particularly difficult challenge in digital maps 
because the axis of the digital maps can be changed 
dynamically. This is the final step in this map making process 
and points of interests are noted down from the probe car data 
and the text is placed so that it is readable and easily located. 
Care has also been taken that the text does not interfere with 
the map's data or design. Different font types, styles, sizes, and 
colors are used to establish clear association between text and 
map features like telephone post, petrol bunks and toll gates. 
Fig. 12 displays a legible point of interest. 













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€ 773:.: 7^37 


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Figure 12. Digital Map with Labeled Points of Interest 

V. Experimental Results 

The outcome of the map making process is a list of 
segments drawn on the map with refined intersections of roads. 
For the purpose of demonstration, the floating car data was 
collected in different roads. Segments for the roads are 
extracted using the segment extraction algorithm. Thereafter 
segment merging was done based on the slope of the adjacent 
road segments. Finally, the road intersection points are refined 
and points of interest are labeled to get the final map. The 



26 



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ISSN 1947-5500 



following experiments demonstrate the simplicity and accuracy 
of the map making. 

A. Simplicity 

A comparison of the map generated by the proposed map 
making process as shown in Fig. 13 versus a digital map 
available in the web as shown in Fig. 14 indicates that the 
proposed process is simple and the map includes only the 
desired routes and points of interest. The generated map can be 
easily interpreted because of its simplicity. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 
more accurate segments depending on the necessity. Two 
different segment extractions for the same floating car data 
with different values of V according to varying requirement is 
shown in Fig. 15, 16. It is to be noted that the nodes are shown 
only to differentiate segments and they are removed in the final 
map. A more accurate and thinner road is shown in Fig. 16 
compared to the one shown in Fig. 15. 



jjji i < -j^tcd- g :s o 




Figure 13. Map generated by the digital map building process discussed in 
this paper 




Figure 14. A previously available digital Map with dense routes and places 

B. Accuracy 

One of the input parameter of segment extraction algorithm 
is the radius of the imaginary circle V. By adjusting the value 
of V the road segment may be made thicker or thinner to get 



Fie Edt VieA Insert Tods Dsion M-d™ -dp 




Figure 15. Map with thick roads with less accuracy 



c i. J s. Vie.-, f.itii ■■:>:<■> te-ion .'.'raw -*.;;, 



jdi > \ -,»8vjj- 3 us . a 




Figure 16. Map with thin roads for the same data with more accuracy 

Accuracy can also be obtained by making the sampling 
sentences interval as minimum. Table II shows a Comparison 
of segments extracted and segments generated after merging at 
different intervals of sampling sentences. Accuracy increases 
with more number of extracted segments and merged segments. 
The graph shown in Fig. 17 gives a comparison between the 



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number of extracted and merged segments for a floating car 
data at different sampling intervals. 



TABLE II. Comparison of segments extracted and segments 

GENERATED AFTER MERGING AT DIFFERENT INTERVALS OF SAMPLING 
SENTENCES. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 
This process can be used to create maps but the process 
depends on a probe car for data collection. In future, this 
drawback can be eliminated by collecting GPS data from the 
GPS enabled vehicles already connected with commercial 
applications. This work can also be extended to handle roads 
with multiple lanes. 



Interval 

of 
sampling 
sentences 
(in No. s) 


No of 

Points 

generated 

by probe 

car data 


No. of 
segments 
extracted 


No. of 
segments 

after 
merging 


Percentage of 

reduction in 

segments after 

merging 


20 


413 


19 


9 


52.63 


40 


413 


8 


4 


50.00 


60 


413 


4 


2 


50.00 


80 


413 


3 


2 


33.33 



o 10 




20 40 60 80 

Sampling Sentences Interleaved 



- No. of Extracted Segments 



- No. of Segments after Merging 



Figure 17. Number of segments generated at different sampling intervals 

The accuracy of the map is inversely proportional to the 
radius of the imaginary circle which is used to draw the road, 
negligible slope and interval of sampling sentences. Desired 
accuracy can be obtained by adjusting these values according 
to the need. 

VI. Conclusion and Future Work 

This paper introduces a customized digital road map 
building process which can be used to build digital maps of 
desired routes. By changing the input parameters, the accuracy 
of the route can be altered to required level and route maps for 
different types of roads, for instance, national highways, state 
highways, city roads and streets can be drawn. The results of 
the map building process discussed in this paper are compared 
with other maps generated by other commercial making 
software and it is found that the proposed process is simple, yet 
powerful. This map making process has eliminated the 
complexity of the previous works carried out in this area and 
customized road map can be built for commercial and other 
applications like vehicle tracking, navigation and route 
guidance systems. 



References 

[I] Stefen Schroedl, Kiri Wagstaff, Seth Rogers, Pat Langley and 
Christopher Wilson., "Mining GPS traces for Map Refinement," Data 
Mining and Knowledge Discovery, vol. 9, pp. 59-87, 2004. 

[2] D. Lee, "The Map-Building and Exploration Strategies of Simple 
Sonar-Equipped Mobile Robot," Cambridge Univ. Press., Cambridge, 
1996. 

[3] P. Weckesser, and R. Dillmann, "Modeling unknown environment with 
a mobile robot,", Robotics Autonomous Systems, vol. 23, pp. 293-300, 
1998. 

[4] Y. L. Ip, A.B. Rad, K.M. Chow and Y.K. Wong, "Segment-based map 
building using enhanced adaptive fuzzy clustering algorithm for mobile 
robot applications," Journal of intelligent and robotic systems, vol. 35, 
pp. 221-245, 2002. 

[5] Lt) WeiFengl,ZHU TongYu, WU DongDong, DAI Hong andHUANG 
Jian, "A heuristic path-estimating algorithm for large-scale real-time 
traffic information calculating," Science in China Series E: 
Technological Sciences, vol. 51, pp. 165-174, Apr. 2008. 

[6] B.W. Parkinson, and J.J. Spilker, "Global Positioning System: Theory 
and Applications," American Institute of Aeronautics and Astronautics, 
Washington, 1996. 

[7] Interface control document. Navstar GPS Space Segment (Navigation 
User Interfaces), 2000. 

[8] Asoke K Talukder and Roopa R Yavagal. "Mobile Computing- 
Technology, Applications and Service Creation," Tata McGraw Hill 
Publishing Company, 2005. 

[9] X. W . Dai, M.A. Ferman, "A simulation evaluation of a real-time traffic 
information system using probe vehicles," IEEE ITSC, vol. 1, pp. 12-15, 
2003. 

[10] R. Kuehne, R.P. Schaefer and J. Mikat, "New approaches for traffic 
management in metropolitan areas," IFAC CTS, 2003. 

[II] T.W. Cheng, D.B. Goldgof, and L.O. Hall, "Fast fuzzy clustering," 
Fuzzy Sets Systems, vol. 93, No. pp. 49-56, 199 8. 

[12] R.N. Dave, "Characterization and detection of noise in clustering," 
Pattern Recognition Lett. vol. 12, pp. 657-664, 1991. 

[13] R.N. Dave, "Use of adaptive fuzzy clustering algorithm to detect lines in 
digital images," Intelligent Robots Computer Vision, vol. VIII, pp. 600- 
611,1989. 

[14] National Marine Electronic Association, http://www.nmea.org, 
accessed on 20.04.2010. 

AUTHORS PROFILE 

G. Rajendran is a second-year Doctoral 

Research Scholar in the Research and 

Development Centre of Bharathiar University. 

He received his Masters degree in Computer 

Applications and M.Phil degree in Computer 

Science from Bharathiar University. He passed 

the National Eligibility Test for lectureship 

conducted by UGC, the apex body of higher 

education in India. He is also working as an 

Assistant Professor of Computer Science at 

Thiruvalluvar Government Arts College, 

Rasipuram, India. His research interests include 

Mobile Computing, Data Mining and programming-language support for 

massive-scale distributed systems. 




28 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 
Dr. M. Arthanari holds a Ph.D. in Mathematics 
from Madras University as well as Masters 
Degree in Computer Science from BITS, 
Pilani. He was the professor and Head of 
Computer Science and IT Department at Tejaa 
Shakthi Institute of Technology for Women, 
Coimbatore, India. At present he is the 
Director, Bharathidhasan School of Computer 
Applications, Ellispettai, Erode, Tamilnadu. He 
holds a patent issued by the Govt, of India for 
his invention in the field of Computer Science. 
He has directed teams of Ph.D. researchers and 

industry experts for developing patentable products. He teaches strategy, 
project management, creative problem solving, innovation and integrated new 
product development for last 36 years. 




y^-^ 



M. Sivakumar has 10+ years of experience in 

the software industry including Oracle 

Corporation. He received his Bachelor degree 

in Physics and Masters in Computer 

Applications from the Bharathiar University, 

India. He is currently doing his doctoral 

research in Anna University, Coimbatore. He 

holds a patent for his invention in embedded 

technology. He is technically certified by 

various professional bodies like ITIL, IBM 

Rational Clearcase Administrator, OCP - 

Oracle Certified Professional 10G, PRINCE2 and ISTQB. His research 

interests include Embedded Technology, Ubiquitous Computing and Mobile 

Computing. 




29 http://sites.google.com/site/ijcsis/ 

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Robust stability check of fractional control 
law applied to a LEO 
(Low Earth Orbit) Satellite 



Ouadia EL Figuigui \ Noureddine Elalami 1 

1 Labor atoire d'Automatique et Inform atique Industrielle 
EMI, Morocco 

( elalami @emi . ac.ma , elfiguigui@gmail . com ) 



Abstract: The use of the differentiation and integration of 
fractional order or non-integer order in systems control is 
gaining more and more interests from the systems control 
community. In this paper we will briefly describe the LEO (Low 
Earth Orbit) satellite systems and recall the theoretical aspects 
of robust stability check procedure. This procedure will be 
applied to a LEO satellite that is under the fractional control 
law. Numerical examples will be analyzed and presented at the 
end of this document 



Keywords: fractional control, LEO satellite, robust stability 
I. .INTRODUCTION 

Recently, a lot attention was given to the problem of 
fractional calculus. There were several works in this area 
[11], [12], [13], [23]. .etc and the author in [1] is presenting for 
the very first time the robust stability checking procedure for 
uncertain fractional order linear time invariant (FO-LTI) 
systems with interval coefficients described in state form. The 
application of such procedure to LEO satellite was new idea. 

The role of an attitude control system for an Earth-pointing 
spacecraft is to maintain the local-vertical/local-horizontal 
(LV/LH) attitude with the presence of different environmental 
disturbances. Most of the time in order to ensure a precise 
pointing, the satellite requires reaction wheel system to 
counteract the attitude drifts caused by those perturbations, 
especially the seculars ones, like torques caused by passive 
gravity gradient, aerodynamic and solar forces. The reaction 
wheels are governed by control laws which dictate the 
amount of torques required to eliminate the drift caused by 
external factors [9]. 

In the attitude control design, different approaches have 
been used, i.e. Proportional Integral Derivative (PID) [18], 
[25], LQR [17], pole placement techniques [14], etc. All those 
methods, expressed in different attitude error terminologies 
are using, very often, Euler angles for small attitude 
commands while for large attitude maneuvers it makes use of 
quaternion [7] and direction cosine errors [18]. 



In [9], the author introduce a fractional control law 
aiming to stabilize the attitude movement of an earth pointing 
satellite under the effect of external disturbances, using 3 -axis 
reaction wheels as actuators. The dynamics of the satellite is 
described by a quasi-bilinear multivariable coupled system. In 
this study, we apply the robust stability check procedure to 
the fractional control law system presented in [9]. 

This paper is organized as follows: In the next section, the 
general nonlinear equations model of an Earth-pointing 
satellite attitude dynamics is developed. Then, in section III, 
the attitude equations are linearized with the nadir attitude 
position as the origin. This leads to a quasi_bilinear 
multivariable coupled system. Then, for small maneuvers, the 
quasi_bilinear term is neglected in order to obtain a linear 
system. In section IV, we recall some theoretical aspects of 
robust stability checking procedure. Forward, in section V, we 
apply this procedure to the LEO satellite system which is 
subject to fractional control law and we share the related 
Numerical results. The conclusion is provided in the last 
section. 



II. NONLINEAR MODEL OF THE SATELLITE 
ATTITUDE DYNAMICS 

The attitude motion of the satellite is represented by the 
Euler equations for the rigid body motion under the influence 
of external moments, such as the control moment generated by 
the actuators. Attitude control requires coordinate 
transformation from LV/LH to The Satellite Coordinate 
System (SCS) system defined as follows: The LV/LH 
coordinate system (X ,F ,Z ) is a right orthogonal system 
centred in the satellite's centre of mass (SCM). The roll 
axis, X , points along the velocity vector, the pitch axis, Y , 
points in the direction of the negative orbit normal and the 
yaw axis, Z , points in the nadir direction. The SCS system 

(X S ,Y S ,Z S ) is a right orthogonal system centred in the 
SCM, parallel to principal moment of inertia axle of satellite. 
Z s is parallel to the smallest moment of inertia axis; Y s is 



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parallel to the largest moment of inertia axis. X s completes 

the right orthogonal system. 

Consider a satellite with three reaction wheels. The general 
nonlinear attitude dynamics model can be described as [18], 
[27] and [21]: 

I(h s I (t)= hjt) co s I (t)Mco s I (t) 

ajUt)Ah w (t) + M s g (t) + P(t) 

where 

> / : Total moment of inertia matrix for the satellite 

without reaction wheels inertia (3x3). 

> co j (t) : Inertial angular velocity vector in SCS (3x1). 

> h w (t) : Angular momentum vector of the wheel 

cluster(3xl). 

> M s (t) : Torques due to Earth's gravity gradient (3x1). 

> P( t ) : Disturbance torque due to aerodynamics, solar 

pressure and other environmental factors. It is assumed 



to be [18], [21]: 



P(t)-- 



4xl0" 6 + 2xl0" 6 sin(^ 
6xl0" 6 +3xl0" 6 sin(^ 
3xl0" 6 +3xl0" 6 sin(^ 



(2) 



where co is the orbital angular rate. 

To keep the satellite attitude earth pointing, the SCS axes 
must remain aligned with LV/LH axes. The transformation 
matrix, expressed with Euler Angles (^, 0, y/) , respectively, 
roll, pitch and yaw angles, is given by [18], [27]: 



(3) 



where: S and C are respectively the sine and the cosine. 
The gravity gradient torque M s g (t) is given by [18], [5]: 



C d C(/) 


c,c, 


s, 


~C<I>\ + ^^e^f 


S*S +S^S 6 S ¥ 


Sf t 


Vv+W,, 


~Sfiy + C ( f,S e S ¥ 


Cfii 



M 



gx 



°Wz 



-I y )s'm(2</))cos z (0) 



M 



gy 



3 ? 
: - g)q (I z - I x ) sin(26>) cos(^) 

M ez =-a>l(I x -I y )smW)sW) 



(4) 



1 gz 



To describe the satellite kinematics, two important factors are 
to be taken into account: angular velocity of the body axis 
frame (SCS) with respect to the reference LV/LH 

frame C0y H (p,q,r) , and the angular velocity of the body frame 
with respect to inertial axis frame co] (co x ,co y ,co z ) . These 

quantities are related to the derivative of the Euler angles as 
follows [18]: 



and 



P = 0~ If/Sg 

q = 6C (p +iyC e S (p 
r = iffC e C^-OS^ 



o)j =(4 H +T VH/S (0 -o) Of 



(5) 



III. LINEARIZED EQUATIONS OF MOTION 

Assuming small variations of the Eulerian angles (^, 0, y/) , 
then the transformation matrix becomes: 

-6 



1 

-¥ 




1 



-# 1 



(6) 



On the other hand, one obtains that: 

^ « p ,0 « q ,\j/ = r 
and 

co x =<f>- co y/, co =0-co o , co z =i// + G> q 



(7) 



(8) 



Then the equations of motion (1) and (5) can be linearized 
about the origin, giving a quasi_bilinear multivariable system: 

x(t) = Ax(t) + Bu(t) 



3 t 



+z?X ^am (c t x(t)) 



i=l 



;x(0) = x Q 



(9) 



+ G §(£)d£ +BP(t) 



where: 

3 



A 



A 



> B^ f W/ (^ (C iX (t))+G \\ u({)d{ 



: Quasi-bilinear term, 



> u(t) = -h w (t) : Control action, 

> x(t) = {(j), 6, y/, (j), 0, y/) : State vector, 
System matrices A, B, C- , G defined as 









0" 



























c 2 = 


-G) Q 





-1 







1 


0_ 















a, 



-co 











-0 O 





0" 










-1 


B = 


1 




0_ 








UI X 










\ll y 











1 



-1 0_ 







1// 



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1 


























A(o\o x 














r S(o\o 1 














mla. 


-mJU 





1 
1 

%{\-o x ) 





B. Robust Stability Check of Fractional System with 
Interval Uncertainties [1]: 

We consider the following FO-LTI system with interval 
uncertain: 




































wjh 











-ojlz 









X (a) (t) = AX(t) + Dw(t) 

where: 

> a is non integer number; 



(13) 



> A^A 



= \a,a\= [a c -AA,A c +zL4jwith 



A + AA 
A c = is a center matrix (normal plant without 



where a i - [i j - I k )/l i for the (i,j,k) index sets (l,2,3), 
(2,3,l), and (3,1,2). 

Moreover, assuming that the angular velocity 
components p , q and r are also small, and for slight 
manoeuvres, one can neglect the quasi bilinear term. 

The equation of motion (9) can then be written in the 
standard form of a linear equations system: 

x( t ) = Ax( t ) + Bu( t ) + BP( t ); x( ) = x (10) 

IV. ROBUST STABILITY CHECK OF 
FRACTIONAL SYSTEM WITH INTERVAL 
UNCERTAINTIES - MATHEMATICAL 
ASPECTS 

In this section, we're recalling the definition of fractional 
system; we're presenting the robust stability checking 
procedure afterwards. 

A. Definition 

In this paper, we consider the Riemann-Liouville 
definition, in which the fractional order integrals are defined 
as 



uncertainties) 



> AA = - 



A-A 



■ is a radius matrix correspondence interval 



uncertainties. 



The stability condition for system (13) is: 

min\arg(l i (A))\>a 7 ^ ; j = l,2,...,iV, \/A EA 7 

In the following subsection, we describe briefly the 
procedure of checking robust stability using the minimum 
argument phase. We proceed by introducing two important 
lemma: 



1. Lemma 1 [1]: 

Define a sign calculation operator evaluated at A c such as: 
P' := sgn Cur v? - uTvT J\ (14) 



where u™ ,v- e ,ul m and v- m are eigenvectors corresponding to 
ith eigenvalue of A c .If P l is constant for all A 1 ,A ^A 1 , then 
the lower and upper boundaries of the real part of ith interval 
eigenvalue are calculated as: 



XI 



■.qHa c -AAoP 1 ) 



(15) 



D af(0 = JfT ) U-(f' 1 f(()d(()M> Q 



T(ii) 



(ID 



where 0" (•) is an operator for selecting the ith real eigenvalue 
= a kj b kj ,and as: 

X? =6; e (A c +AAoP l ) (16) 



and C = Ao B are c kj = a kj b kj ,and as: 



While the definition of fractional order derivatives is 



D£f(t) = ±\p tt (1 >->f(t). 



1. Lemma 2: [1] 

Defining a sign calculation operator evaluated at A c such 



as: 



1 d 
r(l ju)dt 



'jt ()"f(Z)d(Z) 



(12) 



where r( x ) = ^°y x e y dy is the Gamma 

function, (a,t) g IR 2 with a < t and < // < 1 is the order of 
the operation. 

For simplicity we will note D M f{f) or / M (0for Dgf(t) 



if Q l is constant for all A 1 , A 1 ^A 1 , then the lower and upper 
boundaries of the imaginary part of ith interval eigenvalue are 
calculated as: 



l\ m =0 i i m (A c -AAoQ 1 ) 



(18) 



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where #- m (-)is an operator for selecting the ith imaginary 
eigenvalue, and as: 



XT=0™{jE +AAoff) 



(19) 



So, using Lemmas 1 and 2, it is easy to calculate the lower 
and upper boundaries of interval eigenvalue separately in real 
part and imaginary part. From above lemma, if P l and Q l , 
i=\,..., N are calculated, then, interval ranges of 
eigenvalues are finally calculated as: 



X\ e/7' : = f£, X? j+ j]fi\ xr\) (20) 
where j represents imaginary part. We define 

(/) = inf( min|arg/t- ( A)|) i = l,..N 

Since the stability condition is given as ^ > an / 2 , if we 
find sufficient condition for this, the stability can be checked. 
For calculating <f>* , the following procedure can be used: 

PI. Calculate P. and Q t fovi =1,..., N . 

P2. Calculate X? , X\ e , A[ m , and if for all i e {l,2, ...,#}. 
P3. Find arguments of phase of four points such as 

^ = z(/^: m ), tf = z[^J™) 

in the complex plane. 

P4.Find^=inf(^|,|^ 2 |,|^|,|^|}. 

P5. Repeat procedures P3 and P4 for i =1,. . ., N . 

P6. Find/=inf{ #,i = l,...,#} 

P7. If (f > an 1 2 , then the fractional interval system is 
robust stable. Otherwise, the stability of system cannot be 
guaranteed. 

V. ROBUST STABILITY OF LEO SATELLITE 

The aim of this section is to apply the robust stability 
checking procedure subject of the section IV. This procedure 
will be used to proof that our system (10) under the control 
law presented in [9] is robust stable. 

A. Fractional control law 

As mentioned above, the LEO satellite atitude dynamics is 
described, when neglecting the quasi-bilinear term, by the 
system (10): 

x(t) = Ax(t) + Bu(t) +w(t);x(0) = x (21) 



where w(t) = BP(t) is the perturbation term, and u(t) is the 
fractional control law applied in order to stabilize the system 
(10), given by: 

u(t)= K(x(t)-x r ) (a) (22) 

where x r is the attitude reference, equal to zero for nadir 
pointing. 

The linear fractional system is obtained in the form: 

x(t) = Ax(t)-BKx(t) (a) +w(t);x(0) = x (23) 

In the following, only the fractional orders such as 
a = l//?,/?eN* will be considered. Then 



x(t) = Ax{t) - BKx(t) (1//p) + Dw(t) 



(24) 



The equation (24) can be written into the following form: 

X (l/p) (t) = AX(t) + Dw(t) (25) 

We note: 



(26) 



x Vpj \ (t)=x(t), 



*p-i 



P > (t) = * [ P> it) 



and 



X(t) = 



V J 



(?), 



v J 



(t),...,\x 



p-\ 



it) 



(27) 



Id 
Id 
Id 





f °) 












;D = 











K Jd / 



(28) 



Id 
A -BK 



As mentioned above in the subsection IV. 2, the system (26) 
is stable, if and only if: 

n 



minlargA^AM > 



2p 



(29) 



The problem of stabilization by state feedback is equivalent to 
find a matrix K which stabilizes (10); i.e. which checks the 
stability condition given in (29). 

B. Robust stability of the fractional control law 
In this section, we suppose that our system is submitted to 
some perturbations which affect his parameters: 

7. Case 1: Perturbation of co orbital angular rate 



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We consider that, due to the external perturbation, a> 



varies between a>. 



'o 



= co - Aa> 



and 



co n 



a> + Aa> . 



Consequently, A varies between A and A . So our system (26) 

is transformed to FO-LTI system with A e [A /[J . 

For checking the robust stability of system (26), we apply 
the procedure described in section IV. 

If^" >anjl the system (26) is robust stable. Otherwise, 
the stability of system cannot be guaranteed. 

■ Numerical application 
The simulation parameters are the orbital rate 
co =0.00104 rod / sec and the total moment of inertia 
matrix for the spacecraft. 



cw 



o.sr. 




0.75 



m 



4% 



12% 



16% 



20% 



4,020 

3,989 
3,010 



Kg.m 2 



Fig 1: (p* versus 



Aco n 



The fractional control law which stabilizes the system (10) 
with these numerical values is u(t ) = -Kx a with a = 0,5 and 



K 



1 83 0,758 3,530 

9 23,900 2900 7 
3,200 55 



We give below the results of robust stability checking 
procedure applied to the system (26). These results justifies 
the robustness of fractional control laws with respect to the 
variation of o) which can reach ± 20%. 

We note also that the curve of the evolution have a linear 

* Aco 
behaviour and <j> decrease when increase. 



Table I: Results Of Robust Stability Checking Procedure 
For Variation Of 6)« 



Aw 


Aco /co 


co 


co 


(P* 





0% 


0.0010400 


0.0010400 


0.9334 


0.0000416 


4% 


0.0009984 


0.0010816 


0.9060 


0.0000832 


8% 


0.0009568 


0.0011232 


0.8772 


0.0001248 


12% 


0.0009152 


0.0011648 


0.8476 


0.0001664 


16% 


0.0008736 


0.0012064 


0.8178 


0.0002080 


20% 


0.0008320 


0.0010400 


0.7888 



2 .Case 2 : Intrinsic Parameters of LEO Satellite are 
Perturbed 

■ Variation of I x : 
We suppose that I x varied between I x -AI x et 

I x + M x due to external perturbation which modify the form 

of the satellite. 

For the simulation, we consider the same numerical values 
and fractional control law of subsection V-2-1. 

The following table resume the results of robust stability 
checking procedure. 

The system is still robust stable until a variation of ± 20% 

Aco 
of I x . The <p decrease when increase. 

Table Ii:Results Of Robust Stability Checking 
Procedure For Variation Of I x 



AIx 


AIx/Ix 


Ixmin 


Ixmax 


9 





0.00% 


4.0200 


4.0200 


0.9334 


0.1608 


4.00% 


3.8592 


4.1808 


0.8909 


0.3216 


8.00% 


3.6984 


4.3416 


0.8527 


0.4824 


12.00% 


3.5376 


4.5024 


0.8244 


0.6432 


16.00% 


3.3768 


4.6632 


0.8062 


0.8040 


20.00% 


3.2160 


4.8240 


0.8020 



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ISSN 1947-5500 



0-95 



4% 8% 12% 16% 

AIx/Ix 

Fig 2: q>* versus AIx/Ix 



■ Variation of / 

Using the same values as before, we give bellow the results 

of robust stability checking procedure. 

The system is still robust stable until a variation of 

Aco 
± 10,53%. The </> decrease when increase. 



Table Hi: Results Of Robust Stability Checking 
Procedure For Variation Of I 



AI/I 


f 


0% 


0.9334 


2.00% 


0.8889 


4.00% 


0.8457 


6.00% 


0.8165 


8.00% 


0.8163 


10.53% 


0.7897 



<p 




0% 2.00% 4,00% 6.00% 8.00% 10.53% 

AI/I 

Fig 3: (j) versus AI/I 



VI. CONCLUSION 

In this paper, we assume that the orbital angular rate is 
subject to some perturbations and, consequently, the LEO 
satellite system become uncertain. The added value from this 
work is to prove that the fractional order control present in [9] 
is robust stable. We used as method the robust stability 
checking procedure developed in [1]. The mathematical 
aspects of this procedure were recalled. We have studied also 
the case of the perturbation of intrinsic parameters of LEO 
satellite due to the external perturbation which mean that the 
total moment of inertia is uncertain. 

REFERENCES 

[I] Y.Q.Chon, H.S.Ahn, I.Podlubny, Robust stability check of fractional order 
linear time invariant systems with interval uncertainties, signal processing 86 
(2006)2611-2618. 

[2] F.A.Devy Vareta, Pseudo-Invariance Sous Groupe de Transformation :un 
nouveau Concept pour la Commande Robuste, Seminaire Toulousain 
« Representation Diffusive et Application »- N°l-nov. 2000. 

[3] L. Dorcak, I. Petras, I. Kostial and J. Terpak, State space controller design 
for the fractional-order regulated system, ICCC 2001, Korynica, Poland, pp. 
15-20. 

[4] M. M. Dzhebashyan, Integral transforms and representation of functions 
in the complex plan, Nauka Moscou 1966. 

[5] P.C. Hughes, Spacecraft Attitude Dynamics, John Wiley & Sons, USA 
New York, 1986 

[6] B. Kim, E. Velenis, P. Kriengsiri & P. Tsiotras, A Spacecraft Simulator 
for Research and Education, AAS 01-367. 

[7] B.J. Kim, H. Lee and S.D. CHOI, Three-axis Reaction Wheel Attitude 
Control System for KITSAT-3 Microsatellite, IFAC Conference in 
Autonomous and Intelligent Control in Aerospace, Beijing, 1995. 

[8] A. Kailil, Architecture et Analyse Dynamique par la Methode des 
Elements Finis de la plate forme d'un microsatellite, DESA, CRES, 
Mohammedia Engineers School, Maroc, 2000. 

[9] A. Kailil, N. Mrani, M. Abid, M. Mliha Touati, S. Choukri, N. Elalami, 
Fractional regulators for spacecraft attitude stabilization, accepted in the 22nd 
AIAA-ICSSC, Monterey, California 9-12 May 2004. 

[10] S.Ladaci,JJ.Loiseau,A.Charef,Stability Analysis of Fractional Adaptive 
High-Gain Controllers for a class of Linear Systems General case,2006, 
IEEE. 

[II] S.Ladaci,A.Charef, Mit Adaptive Rule with Fractional Integration, in 
Proceedings CESA 2003 MACS Multiconference Computational 
Engineering in Systems Applications, Lille, France. 

[12] S.Ladaci, A.Charef, On Fractional Adaptive Control, nonlinear 
Dynamics, vol.43 ,n°4, March 2006. 

[13] S.Ladaci, A.Charef, An Adaptive Fractional PI D. Controller, in 
Proceedings TMCE 2006 international Symposium series on Tools and 
Methods of Competitive Engineering, Ljubljana, Slovenia, April, Aprill8-22. 

[14] CH. Lubich, Discretized fractional calculus, SIAM J. Math. Anal. Vol. 
17 No. 3 May 1986. P 

[15] D. Matignon, Fractals et loi d'echelle, Edition Hermes 2002. 

[16] D. Matignon, Stability results for fractional differential equations with 
applications to control processing, in Computational Engineering in Systems 
and Application multiconference, vol. 2, pp. 963-968, MACS, IEEE-SMC. 
Lille, France, July 1996. 

[17] K.L. Musser, W.L. Elbert, Autonomous Spacecraft Attitude Control 
using Magnetic Torquing only, Proceeding of flight mechanics estimation 
theory symposium, NASA, 1986, pp. 23-38. 

[18] M. J. Sidi, Spacecraft Dynamics and control, Cambridge University 
Press, Cambridge, UK, 1997. 



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[19] S.G. Samko, A. A. Kilbas & O.I. Maritcheu, Fractional integrals 

and derivative: theory and application, Gorden & Breach, 1987. 

[20] A. Skullestad, J. Gilbert, H ^ Control of a Gravity Gradient Stabilised 

Satellite, Control Engineering Practice 8 (2000) 975-983. 

[21] P. Tsiotras, H. Shen and C. Hall, Satellite Attitude Control and Power 
Tracking with Momentum Wheels, AAS 99-317. 

[22] C. Valentin-Charbonnel, G. Due and S. Le Ballois, Low-order Robust 
Attitude Control of an Earth Observation Satellite, Control Engineering 
Practice 7 (1999) 493-506. 

[23] B.M.Vinagre,I.Petras, I.Podlubny and Y.Q.Chen, Using Fractional Order 
Ajustement Rules and Fractional Order References Models in Model- 
Reference Adaptive Control, Nonlinear Dynamics, vol.29. 

[24] B. Wie, Space Vehicle Dynamics and Control, AIAA Education Series, 
1998. 

[25] C. Witford, D. Forrest, The CATSAT Attitude Control system, 
Proceeding of the 12 th Annual AIAA/USU Conference on Small Satellite, 
1996. 

[26] C. H. Won, Comparative study of various methods for attitude control of 
LEO satellite, Aerospace Science and Technology. 1270-9638. 99/05/ 
Elsevier, Paris. 

[27] J. R.Wertz, Spacecraft Attitude Determination and control, Kluwer 
Academic Publishers, Dordrecht, Holland, 1978. 

[28] K. Zhou, JC. Doyle & R. Glover, Robust and Optimal control, Prentice 
Hall, 1996 



36 http://sites.google.com/site/ijcsis/ 

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PERFORMANCE EVALUATION OF 

GENETIC ALGORITHM FOR SOLVING 

ROUTING PROBLEM IN 

COMMUNICATION NETWORKS 



Ehab Rushdy Mohamed 

Faculty of Computer and Informatics 

Zagazig University 

Zagazig, Egypt 

ehab. rushdy (cb^mail. com 



Ibrahim Elsayed Zidan 

Faculty of Engineering 

Zagazig University 

Zagazig, Egypt 

ibrahim.zidan. I23(cb^mail. com . 



Mahmoud Ibrahim Abdalla 

Faculty of Engineering 

Zagazig University 

Zagazig, Egypt 

mabdalla201 OffigmgiL com 



Ibrahim Mahmoud El-Henawy 

Faculty of Computer and Informatics 

Zagazig University 

Zagazig, Egypt 

. i. m. elhenawy&gmail. com . 



Abstract — There has been an explosive growth in both 
computer and communication networks since the last 
three decades. Communication networks pervade our 
everyday life, from telephony system, to airline 
reservation systems, to electronic mail services, to 
electronic bulletin boards, to the internet. Routing 
problem is one of the most important issues facing the 
development, improvement and performance of 
communication networks. Recently, there has been 
increasing interest in applying genetic algorithms to 
problems related to communication networks. This study 
evaluates the genetic algorithm used for finding the 
shortest path in communication network. The paths 
result from applying genetic algorithm could be used in 
establishing routing table for network protocols. The 
genetic approach is thought to be an appropriate choice 
since it is computationally simple, provide powerful search 
capability, and has the ability to move around in the 
solution space without a dependence upon structure or 
locality. 

The performance of the genetic algorithm is compared 
to Dijkstra algorithm, which is widely used in most 
network protocols. This is realized using different 
simulated networks to clarify the advantages and 
deficiencies of each algorithm. The relative performance 
of the two algorithms is judged on the basis of delay and 
adaptation. 



I. Introduction 

One of the most common problems encountered in 
analysis of networks is the shortest path problem: 
finding a path between two designated nodes having 
minimum total length or cost. In many applications, 
however, several criteria are associated with traversing 
each edge for a network. For example, cost and time 
measures are both important in most networks, as are 
economic and ecological factors. As a result, there has 
been recent interest in solving the shortest path problem 
and many algorithms are proposed to solve this problem 
[1], [2], [3], [4]. Shortest path problem is a classical 
research topic. It was proposed by Dijkstra in 1959 and 
has been widely researched [5]. 

A. Dijkstra Algorithm 

The Dijkstra algorithm is considered as the most 
efficient method. It is based on the Bellman 
optimization theory. Dijkstra routing algorithm is 
widely used in many applications because it is very 
simple and easy to understand. This algorithm finds the 
shortest paths from a source to all other nodes. To do 
this it requires global topological knowledge (the list of 
all nodes in the network and their interconnections, as 



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well as the cost of each link). In most general the 
weight of each link could be computed as a function of 
the distance, bandwidth, average traffic, communication 
cost, mean queue length, average delay, and other 
factors. In most general the weight of each link could 
be computed as a function of the distance, bandwidth, 
average traffic, communication cost, mean queue length, 
average delay, and other factors [6] . 

The parameter D(v) is considered as the distance 
( sum of links weights along any path) from source 
node 1 to node v, and the parameter L(i,j) is 
considered as the given cost between node i and nodey. 
There are then two parts to the algorithm: an 
initialization step, and a step to be repeated until the 
algorithm terminates: 

1 . Initialization 

Set N = {!}. For each node v not in Set TV, D(v) is set 
to L(l,v). The value oo is used for nodes that are not 
connected to node 7; any number larger than maximum 
cost or distance in the network would suffice. 

2. At each subsequent step 

Find a node w not in N for which D(w) is a minimum 
and add w to N. Then D(v) is updated for all nodes 
remaining that are not in N by computing 
D(v )= Min [D(v) , D(w)+L(w,v)J 

Step 2 is repeated until all nodes are in N. 

The Dijkstra algorithm is widely used in most 
popular routing protocols because of its simplicity and 
efficiency. But when the network is very big, then it 
becomes inefficient since a lot of computations need to 
be repeated. The efficient set of paths may be very large, 
possibly exponential in size. Thus the computational 
effort required for solving the problem can increase 
exponentially with the problem size [7]. 



B. Genetic Algorithm Mechanism 

The genetic approach is a special kind of stochastic 
search algorithm, it based on the concept of natural 
selection and genetics. It provides a powerful search 
capability and has the ability to move around in the 
solution space without the structure and locality. This 
approach identifies solutions that are closest to the ideal 
solution as determined by some measures of distance. 
Genetic algorithm constitutes the increasingly large part 
of evolutionary calculation techniques, which form the 
artificial intelligence .As it's obvious in its name, 
genetic algorithm, forming evolutionary popular 
technique, inspired from evolution theory of Darwin. 
Any kind of problems which involves genetic algorithm 
is solved through the application of artificial evolution 
technique. Genetic algorithm is used to solve problems 



that are hard to be solved by applying conventional 
methods. In general terms, genetic algorithm has three 
field of application. They are; optimization, practical 
industrial applications, and categorization systems [8], 
[9]- 

Genetic Algorithm starts with a set of solution, 
which is identified with chromosomes and known as 
population. Resolutions that have come out from a 
population are applied to the next one with the 
expectation of positive improvements. The selected 
group is used for creation of a new population 
according to their compatibility. Nevertheless, it's 
likely that the compatible ones produce better solutions. 
This would be continued until the expected solutions 
are obtained. A simple genetic algorithm consists of the 
following steps: 

1. Initialization: a random population of n 
chromosomes (appropriate solution of the problem) is 
created. 

2. Fitness: each x chromosome is evaluated using the 
fitness f (x). 

3. New population: a new population is created; this is 
done by repeating the following steps until the new 
population is complete 

a. Selection: two parental chromosomes are selected 
from the population according to their fitness. 

b. Crossover: a new member is created; parents are 
cross-fertilized according to possibility of crossover. If 
cross-fertilization is not applied, new member is a copy 
of a mother or father. 

c. Mutation: the place of the new member is 
changed according to the possibility of fission. 

d. Addition: the new member is added to the new 
population. 

4. Alteration: new generated population is used when 
algorithm is re-applied. 

5. Test: If the result is convincing, algorithm is 
concluded and the last population is presented as the 
solution. 

6. Cycle: Return to the second step. 

As seen above, the structure of the genetic algorithm 
is quite general and can be applied to any kind of 
problem. Identification of chromosomes is generally 
done by the numbers in double set. Members that are 
used for the crosswise must be selected among the best 
ones. There is no completion criterion of GA. Having a 
satisfactory result or guaranteeing the convergence 
could be used as criteria for completion of the 
algorithm. 

The most important parts of GA are the processes of 
crossover and mutation. These processes are started 
with a unit of probability, and in most of the cases 



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applied randomly. This helps to get satisfactory results. 
A chromosome should include information on solution 
that it represents. Each chromosome is set up with 
binary series. Each number that named bit in this series 
can represent a characteristic of the solution. Or, a 
serial, on its own, would indicate a number. Expressing 
the chromosomes with the set of numbers in the binary 
series is the most common representation form; 
however, also integer and real numbers can be used [9] . 
The reason for selecting of binary series is as the 
following: first of all it is simple, and secondly, it is 
processed by the computer easier and faster. The 
reproduction process is a process which is applied 
according to certain selection criteria to reproduce new 
generation. A selection criterion takes the compatibility 
as a basis and selects the compatible members. At later 
stage, it is possible that more compatible new members 
emerge from those members that are subjected to 
crosswise and fission. All members may be selected in 
terms of compatibility or some are selected randomly 
and transferred to next generation. Crossover can be 
applied after the decision for representation of 
chromosomes taken. Crossover is a process which is 
applied through the deduction of some genes from 
parents to create new members. There is a need of 
selection of individuals to constitute parents. According 
to theory the fittest individuals must be survive to leave 
descendants. This selection can be based on several 
criteria. Examples are Roulette selection, Boltzmann 
selection, tournament selection, sorted selection [8]. 

Genetic algorithms, as powerful and broadly 
applicable stochastic search and optimization 
techniques, are the most widely known types of 
evolutionary computation methods today. Recently, the 
genetic algorithm community has turned much of its 
attention to optimization problems in the field of 
communication and computer networks, resulting in a 
fresh body of research and applications [10], [11], [12], 
[13], [14], [15], [16]. 

II. Genetic Approach for Solving the Shortest 
Path 

Routing problem is one of the most important issues 
facing the development, improvement and performance 
of communication networks. Many ideas and methods 
have been proposed to solve routing problems [17], 
[18], [19], [20], [21]. One of the most common 
problems encountered in networks is the shortest path 
problem. The shortest path problem is defined as that of 
finding a minimum length or cost path between a given 
pair of nodes. Shortest path problem is a classical 
research topic. 



A. Problem Formulation 

An undirected graph G = (V,E) comprises a set of 

nodes V={1,2, ,n} and a set of edges Eg VxV 

connecting nodes in V. For each edge in the graph, there 
is a nonnegative number Cy represents the cost, distance, 
and others of interest, from node i to node j. A path 
from node i to node j is a sequence of edges 
(i,l),(l,m),....,(kj) from E in which no node appears 
more than once. A path can also be represented using a 
sequence of nodes (i,l,m...k,j). x tj is an indicator 
variable. It is equal 1 if the edge (i,j) is included in the 
path and zero otherwise. 

The shortest-path problem is formulated as follows: 



minimize 



f( x ) = HH c u x u 



subject to ^jc u <% VieV 



a) 



(2) 



X x, > x lk , V(i,k) e E, Vi e V\{ 1 ,n} (3) 



E x iy=E x y« =1 ' V (i'J) e 



V 



(4) 



where constraints (2) and (3) together imply that any 
node other than nodes land node n has either or 2 
nonzero incident edges. Constrain (4) makes node land 
n the endpoints of the path [9]. 

B. Encoding The Path Of The Graph 

The difficult part of developing a genetic algorithm 
for this problem is how to encode a path in graph into a 
chromosome. A priority based encoding method can 
potentially represent all possible paths in a graph. 
Besides, the capability of representing a path for 
undirected graph is one of the most significant features 
of the priority based encoding method. This difficulty 
of finding a representation for the paths for a graph in 
genetic approach arises because of the existence of 
variable number of nodes in the paths, and the available 
number is n-1 for an ft-order graph. Also, a path 
consists of consecutive prepared number of edges. A 
random sequence of edges doesn't correspond to a path. 
These problems were solved by encoding some guiding 
information for establishing a path in a chromosome but 
not a path itself. 

Any chromosome is defined by two factors. The first 
is locus that represents the position of gene in the 
structure, while the second is allele represents the value 



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of gene. Simply, the node is represented by locus and 
the priority of representing the node in constructing a 
path with respect to other ones. 

The mapping between encoding and path is many-to- 
one, which means that different chromosomes may 
produce an identical shortest path. But it is easy to 
prove that the probability of occurrence of many-to-one 
mapping is very low [8]. Therefore, in most cases, there 
are no trivial genetic operations associated with the 
encoding. It is also easy to verify that any permutation 
of the encoding corresponds to a path, so that most 
existing genetic operators can easily be applied to the 
encoding. Also, any path has a corresponding encoding; 
therefore, any point in solution space is accessible for 
genetic search. 

A path growth procedure is considered to generate a 
path from chromosomes. The procedure generates a 
path from initial node to end node by appending 
eligible edges into the path consecutively. At each step, 
several edges are considered. The edge added to a 
partial path is always the edge incident to the node with 
the highest priority, which extends the partial path from 
the terminal node [9] . 

C. Genetic Algorithm Procedure 

Chromosomes are evaluated using a measure of fitness 
during each iteration of a genetic algorithm. There are 
three major steps included in the evaluation: 

1 . Convert the chromosome to a path. 

2. Calculate the objective values. 

3. Convert the objective values to fitness values. 

The roulette wheel approach, a type of fitness- 
proportional selection, was adopted as the selection 
procedure. The elitist method was combined with this 
approach to preserve the best chromosome in the next 
generation and overcome stochastic errors of sampling. 
With the elitist selection, if the best individual in the 
current generation is not reproduced in the new 
generation, one individual is removed randomly from 
the new population and the best one is added to the new 
population [7], [8]. 

Genetic approach uses the comprise approach based 
fitness assignment in solving the shortest path problem. 
The compromise approach is regarded as a type of 
mathematical formulation of goal-seeking behavior in 
terms of a distance function. The compromise approach 
identifies solutions that are closest to optimum. The 
overall genetic approach is illustrated as follows: 

• Step 1 : The data are entered and genetic parameters 
are set. 

• Step 2: The initial population is generated 
randomly. 

• Step 3: The paths are encoded into chromosomes. 



• Step 4: The parent chromosomes are selected 

• Step 5: The objectives are calculated and evaluated 
for each individual. 

• Step 6: The next generation is produced by 
applying crossover and mutation to the parent 
chromosomes. 

• Step 7: The new generations replaced the current 
ones. 

• Step 8: If the maximum generation is reached then 
procedure is stopped; otherwise; go to step 4. 

III. Simulation and Results 

Simulation experiments are carried out for a number 
of networks with different size. The objective is to 
investigate and evaluate the performance of the genetic 
algorithm and Dijkstra algorithm as well using different 
simulated networks .The sizes of simulated networks 
considered are 30, 50, 100, 120 and 150 nodes. 
Assumptions for cost links are considered for the 
simulated networks. The topology of 30-node simulated 
network used is illustrated in Figure 1 . 




Fig. 1. The topology of 30-node simulated network 



It should be pointed out that the performance of the 
genetic algorithm depends on the choice of the 
parameters like population size, crossover rate and 
mutation rate. In this simulation, various values of these 
parameters are experimented. The optimal values are 
found to be 800, 0.6 and 0.1 for population size, cross 
over rate and mutation rate respectively as illustrated in 
Table I. 

TABLE I. The Initial Values for Genetic Algorithm 



Parameter 


Value 


Population size 


800 


Mutation rate 


0.6 


Cross over rate 


0.1 


Number of generation 


100 



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As an example, Table II shows the source and 
destination for each pair of nodes in the 30-node 
simulated network to be applied on both genetic 
algorithm and Dijkstra algorithm. 

TABLE II. The Four Source-destination Pairs 

FOR 30-NODE SIMULATED NETWORK 



TABLE IV. The Results of Applying the Genetic Algorithm 
on the 2 ND Route in 30-node Simulated Network 



Case 


Source 
node 


Destination 
node 


1 


Nil 


N12 


2 


N8 


N25 


3 


N6 


N29 


4 


NO 


N22 



Path 


Cost 


Running 

time in 

sec 


Generation 
number 


N8-N6-N24-N23-N2- 
N22-N4-N25 


67.16 


10.034 


200 


N8-N24-N6-N28-N14- 
N11-N25 


62.39 


15.786 


300 


N8-N28-N14-N11-N4- 
N25 


57.70 


21.983 


400 


N8-N2-N15-N4-N25 


43.70 


25.556 


500 


N8-N6-N22-N11-N25 


25.54 


34.984 


700 


N8-N6-N22-N11-N25 


25.54 


45.658 


900 


N8-N6-N22-N11-N25 


25.54 


48.772 


1000 



Table III shows the results of implementing the 
genetic algorithm for the first route in the 30-node 
simulated network. Table IV shows the results of 
implementing genetic algorithm for the second route in 
the 30-node simulated network. The results of 
implementing genetic algorithm for the third route in 
the 30-node simulated network is illustrated in Table V. 
Table VI shows the results of implementing genetic 
algorithm for the fourth route in the 30-node simulated 
network. Figure 2 illustrates the variation of cost with 
respect to number of generation for the four routes in 
the 30-node simulated network. 

Table VII illustrates the results of applying Dijkstra 
algorithm on the four pairs of nodes in the 30-node 
simulated network. 



TABLE V. The Results of Applying the Genetic Algorithm 
ON THE 3 rd Route in 30-node Simulated Network 



Path 


Cost 


Running 
time in sec 


Generation 
number 


N6-N8-N28-N16-N27- 
N12-N19-N29 


71.71 


10.656 


200 


N6-N8-N16-N27-N9- 
N12-N19-N17-N29 


53.43 


14.331 


300 


N6-N8-N16-N27-N12- 
N19-N17-N29 


50.26 


19.711 


400 


N6-N8-N16-N27-N9- 
N12-N29 


49.24 


25.000 


500 


N6-N8-N28-N16-N27- 
N12-N29 


46.07 


29.886 


600 


N6-N8-N28-N16-N27- 
N12-N29 


46.07 


33.223 


700 


N6-N8-N28-N16-N27- 
N12-N29 


46.07 


38.119 


800 



TABLE III. The Results of Applying the Genetic Algorithm 
ON THE 1 st Route in 30-node Simulated Network 



TABLE VI. The Results 
on the 4 th ROUTE 



of Applypng the Genetic Algorithm 
pn 30-node Simulated Network 



Path 


Cost 


Running 
time in sec 


Generation 
number 


N11-N20-N18-N7-N1- 
N10-N9-N12 


114.82 


4.449 


100 


N11-N22-N14-N18-N10- 

N1-N12 


91.30 


10.556 


200 


N11-N22-N4-N25-N3- 
N10-N1-N12 


88.66 


14.008 


250 


N11-N22-N2N-N8-N16- 
N27-N12 


77.46 


19.892 


400 


N11-N3-N10-N9-N12 


59.18 


29.327 


600 


N11-N3-N10-N9-N12 


59.18 


34.563 


700 


N11-N3-N10-N9-N12 


59.18 


39.437 


800 



Path 


Cost 


Running 

time in 

sec 


Generation 
number 


N0-N21-N13-N7-N18- 
N14-N28-N6-N22 


116.67 


15.980 


300 


N0-N1-N19-N29-N12- 
N27-N16-N28-N14-N22 


96.65 


22.879 


400 


N0-N13-N5-N7-N18- 
N14-N11-N22 


79.94 


27.870 


500 


N0-N13-N7-N18-N14- 
N11-N22 


71.63 


40.676 


800 


N0-N1-N10-N8-N6-N22 


66.06 


51.922 


1000 


N0-N1-N10-N8-N6-N22 


66.06 


54.398 


1100 


N0-N1-N10-N8-N6-N22 


79.94 


60.933 


1200 



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150.00 - 

J 00.00 ■ 

o 

J 50.00 ■ 

0.00 ■■ 



s! versus generation number for the first route 



100 200 250 400 600 700 300 
feneration number 



Cost versus generation number for the third route 




200 300 400 500 600 700 800 
Generation number 



Cost versus generation number for the second route 
80.00 T 




200 300 400 500 700 900 1000 
Generation number 



_ Cost versus genertaion number for the 4th route 




300 400 500 800 1000 1100 1200 
Generation number 



Fig. 2. Variation of cost with respect to number of generation 
for 30-node simulated network 



network size. On the other side, Dijkstra algorithm is 
not affected with the increase of the network size. 

TABLE VIII. Comparison Between the Genetic Algorithm 
and Dijkstra Algorithm in Accordance to Running Time 



Simulated 
Network 


Average running time in sec 


Genetic 
algorithm 


Dijkstra 
algorithm 


30-node network 


36.530 


0.126 


50-node network 


43.830 


0.093 


100-node network 


48.440 


0.112 


120-node network 


55.135 


0.137 


1 50-node network 


69.055 


0.106 



TABLE VII. The results of applying Dijkstra Algorithm on 
the 4 th Route in 30-node Simulated Network 



Case 


Path 


Cost 


Time 
in sec 


1 


N11-N3-N10-N9-N12 


59.18 


0.125 


2 


N8-N6-N22-N11-N25 


25.54 


0.058 


3 


N6-N8-N28-N16-N27- 
N12-N29 


46.07 


0.086 


4 


N0-N1-N10-N8-N6-N22 


66.06 


0.172 



It is pointed out that the genetic algorithm can find 
the optimum path that is exactly obtained by applying 
Dijkstra algorithm. Furthermore, various candidate 
paths close to optimum are obtained using the genetic 
algorithm. Also, the associated time to get various 
paths and generation number are illustrated in these 
tables. It is noted that increasing the generation number 
leads to get paths more close to optimum as shown in 
Figure 2. Also, paths more close to optimum are 
consumed more time than others. 

The same results are achieved using simulated networks 
with size of 50, 100, 120 and 150 nodes. 
The only concern during implementing the genetic 
algorithm is that genetic algorithm needs much running 
time compared to Dijkstra algorithm, this is illustrated 
in Table VIII. It is noticed that Dijkstra algorithm is 
working efficiently and it is implemented in the 
permitted time with network with size till 150 nodes. 
Also, the relation between the average running time of 
the genetic algorithm and the network size is illustrated 
by applying the genetic algorithm on different 
simulated networks of 30, 50, 100, 120 and 150 nodes. 
It is noticed from the results shown in Figure 3 that the 
running time needed to implement the genetic 
algorithm is increased as result of increasing the 



Average running time of genetic algorithm versus 


Average running time of Dijkstra algorithm versus 


network size 


network size 









o 0.160 1 
| 0.140 ■ 






u 80.000 ■ 
E 






^K 


ning t 


^^4^ 




di 0.120 ■ 
c o 0.100 ■ 


X^^ 




3 « 40.000 ■ 
lii - c 


^^ 




§ 8 0.080 ■ 
v £ 0.060 ■ 






jjj 1 20.000 ■ 

to 






in 0.040 ■ 
jji 0.020 ■ 






3 0.000 \ 






< 0.000 \ 










30 50 100 120 150 


30 50 100 120 150 


node node node node node 


node node node node node 


Network size 


Network size 



Fig. 3. The average running time of the genetic algorithm and 
Dijkstra algorithm using five simulated networks with different 
sizes 



Therefore, the genetic algorithm is considered to be a 
promising algorithm that produces multiple paths close 
to optimum in addition to the optimum path itself and 
also can combine with other algorithm to form a hybrid 
technique that can be used in multiple applications and 
environments. 

IV. Conclusion 

In this study, the analysis and performance 
evaluation of the genetic algorithm that used to solve 
the shortest path problem are presented. The genetic 
algorithm is experimented using different simulated 
networks with sizes of 30, 50, 80, 100,120 and 150 
nodes. A comparison between both the genetic 
algorithm and Dijkstra algorithm is illustrated .It should 
be pointed out that the performance of the genetic 
algorithm depends on the choice of the parameters like 
population size, crossover rate and mutation rate. In the 
simulation, various values of these parameters are 
experimented before selecting the ones that achieve the 
desired results. The desired values of population size, 
crossover rate and mutation rate parameters are 800, 0.6 



6 
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and 0.1 respectively. From the testing and results 
analysis, the following results are achieved: 

1. Genetic algorithm is able to find the optimum 
solution achieved by Dijkstra algorithm. 

2. Increasing the number of generation leads to 
obtaining paths more close to optimum 

3. Genetic algorithm achieves the desired results with 
much running time comparing to Dijkstra algorithm 

4. The running time of implementing the genetic 
algorithm is increased as a result of increasing the 
network size. 

5. Dijkstra algorithm is working efficiently and it is 
implemented in the permitted time with network with 
size till 150 nodes 

6. Genetic algorithm is able to find alternative paths 
close to the shortest path; consequently, these results 
can be used in establishing routing table for nodes. 
These alternative paths can be generated using different 
genetic operators, which can be invoked at a specific 
probability. As a result, the load and utilization of the 
paths in communication networks will be slightly 
reduced and load balance can be achieved using 
multiple paths close to optimum to route traffic between 
two nodes. 

References 

[1] D. Awduche, A. Chiu, A. Elwalid, I. Widjaja, and X. Xiao, 

"Overview and principles of Internet traffic engineering", 

Internet RFC 3272, May 2002. 
[2] [2]A. Montes, "Network shortest path application for optimum 

track ship routing", California, 2005. 
[3] S. F. Wu, F. Y. Wang, Y. F. Jou, and F. Gong, "Intrusion 

detection for link-state routing protocols", In IEEE Symposium 

on Security and Privacy, 1997. 
[4] M. T. Goodrich, "Efficient and Secure Network Routing 

Algorithms", Johns Hopkins University, 2001. 
[5] E. Dijkstra, "A note on two problems in connection of graph", 

Numerical Mathematical, 1:269-271, 1959. 
[6] M. Pioro, D.Medhe, Routing, Flow, and Capacity Design in 

Communication and Computer Networks, Morgan Kauffman 

series, 2004. 



[7] Y. Li, R. He, Y. Guo, "Faster Genetic Algorithm for Network 

Paths", The Sixth International Symposium on Operations 

Research and Its Applications (ISORA'06), China, 2006. 
[8] M. Gen, R. Cheng, Genetic Algorithms & Engineering 

Optimization, Wiley Series in Engineering and Automation, 

2000. 
[9] D. Goldberg, Genetic Algorithms in Search, Optimization, and 

Machine learning, Addison Wesley, 1989. 
[10] P. Sateesh, S. Ramachandram," Genetic Zone Routing Protocol" 

Journal of Theoretical and Applied Information Technology, 

2008. 
[11] M. R. Masillamani, A. V. Suriyakumar, G. V. Uma, "Genetic 

Algorithm for Distance Vector Routing Technique", AIML 06 

International Conference, Sharm EL Sheikh, Egypt, 2006. 
[12] N. Selvanathan and W. Jing, "A genetic algorithm solution to 

solve the shortest path problem in OSPF and MPLS", Malaysian 

Journal of Computer Science, Vol.16 No. 1, 58-76, 2003. 
[13] J. Cunha, "Map algorithm in routing problems using genetic 

algorithm, IFORS Triennial Conference - Edinburgh/Scotland, 

2002. 
[14] B. Fortz, M. Thorup, "Internet traffic Engineering by 

Optimization OSPF Weights", Proc. IEEE Infocom, 2000. 
[15] M. Ericsson, M.G.C. Resende, P.M. Pardalos, " A Genetic 

Algorithm for the weight setting problem in OSPF routing", 

Journal of Combinatorial Optimization, 2001. 
[16] O. Akbulut, O. Osman, O. N. Ucan, "Computer Network 

Optimization Using Genetic Algorithm", The journal of 

Electrical & Electronics Engineering, Istanbul University, Vol. 

6, 245-250, 2006. 
[17] D. Staehle, S. Koehler, U. Kohlhhass, "Towards an optimization 

of IP Routing by Link Cost Specification ", University of 

Wurezburg, 2000. 
[18] J. Milbrant, S. Koehler, D. Staehle, L. Berry, " Decomposition 

of Large IP Networks for Routing Optimization", Technical 

Report 293, University of Wurezburg, 2002. 
[19] G. L. Li and P. W. Dowd , " An analysis of network 

performance degration induced by workload fluctuations", 

IEEE/ACM Trans, on networking, 3, No. 4, 433-440,1995. 
[20] F. Xiang, L. Junzhou, W. Jieyi, G. Guanqun, " QoS routing 

based on genetic algorithm", Computer Communication 22, 

1392-1399, 1999. 
[21] K. Vijayalakshmi, S. Radhakrishnan, " Dynamic Routing to 

Multiple destinations in IP Networks using Hybrid Genetic 

Algorithm" International Journal of Information Technology, 

2008. 



7 
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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol.8,No.3,2010 



Testing Equivalence of Regular Expressions 



Keehang Kwon 

Department of Computer Engineering 

Dong-A University 

Busan, Republic of Korea 

khkwon@dau.ac.kr 



Hong Pyo Ha 

Department of Computer Engineering 

Dong-A University 

Busan, Republic of Korea 

hompoyo@hotmail.com 



Abstract — We propose an algorithm that tests 
equivalence of two regular expressions. This 
algorithm is written in the style of a sequent proof 
system. The advantage of this algorithm over 
traditional algorithms is that it directly captures the 
real essences regarding language equivalence. As a 
consequence, our algorithm extends easily to other 
larger languages with variables. 

Keywords- regular expression, equivalence, 
language, algorithm. 



l.INTORDUCTION 



easy to understand, nondeterministic and some 
resemblance to the proof theory of intuitionistic 
linear logic [2]. 

In addition, it is a simple matter to observe that this 
algorithm extends well to more general grammars. 
Our algorithm thus captures the essence of 
language equivalence. In this paper we present our 
algorithm, show some examples of its workings, and 
discuss further improvements. The remainder of this 
paper. The remainder of this paper is structured as 
follows. We describe our algorithm in the next 
section. In Section 3, we present some example. 
Section 4 concludes the paper with some 
considerations for further improvements. 



Regular expressions [1] have gained much interest 
for applications such as text search or compiler 
components. One central test related to regular 
expressions are to test, given two regular 
expressions, whether two regular expressions are 
equivalent. The equivalence of regular expressions 
is useful for simplifying regular expressions. For 
example, + 01* can be simplified to 01*. 
Unfortunately, the traditional algorithms [4] focus 
too much on the equivalence of regular expressions 
and finite automatas. As a consequence, the 
resulting algorithm is not so intuitive, as they 
convert regular expressions to finite automatas for 
testing equivalence. 

Furthermore, this technique does not extend well to 
other grammars such as context-free grammars such 
as context-free grammars. 

This paper introduces an algorithm for testing 
equivalence of regular expressions. It is simple, 



2.THE LANGUAGE 

The -free regular expression is described by G- 
formulas given by the syntax rules below: 

G\\=s\a\G*G\G+G\G* 

In the rules above, a represents the set {a}, s 
represents { s }. represents the empty set. F*G 
represents the concatenation of two sets F and G. 
The Kleene closure of G-G*- indicates there are 
many number of G, i.e., G m ... m G. We write GG in 
place of G*G. 

The regular expressions have a number of laws for 
their equivalence. 

For instance, is the identity for union: + L and 
L +0= L where L is any regular expression. 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol.8,No.3,2010 

Similarly, s is the identity for concatenation: sL = w is in a regular expression r can be converted to 

Ls=L. the question of whether w is a subset of r. 



On the other hand, is the annihilator for 

concatenation : 0L-L 0= 0. 

The question of whether two regular expressions are 
equivalent is quite complex. We will present an 
algorithm for this task in the style of a proof system. 
Let G be a regular expression and i~i,. . .,/" n be a list 

of regular expressions. Then 7\, . . . , r n \- G - the 

notion that G is subset of the concatenation of 

A,- • ., r n - is defined as follows : 



3. Example 



This section describes the use of our algorithm. 
An example of the use of this construct is 
provided by the following equivalence: (/ ) = 
/".This equivalence follows from the facts that 
(/ ) is a subset of / and vice versa. These are 
derived below. 



Algorithm for Subset Relation 

r\-A 



eL 



e,r\-A 



he 



eR 



r v R\-p - ^ 



R 



(/*)* \-t- 



The proof of this as follows: 



1 \- I - Axiom 



■Axiom — pr R 

r\-p-\-ii; 

r,j>\-A 
^ r, P -vhA * 



p\-p 

r,p\-v 
r,p+v\-A 



^ ^ 



r,o,p^A CL r^A DL 



r,p\-A 



r,p \-a 



FhA WL ^PR 



r,p \-a 



r \-p 



In the above rules, r, A denote a list of regular 

expressions and p,cp denote a single regular 
expression. In proving p • <p from r, it splits r 
into two disjoint parts.. In dealing with p 
construct, the proof system can either discard it, 
or use p at least once. 

The above algorithm also tests membership in a 
regular language: the question of whether a string 



(1)/* |- / -DL 
(2) / * |- / * - PR 
(3)/> (/*)* -PR 

The other direction, / * |- (/*)*' is proved below. 



/ |- / 



Axiom 



(1)/* |- / -DL 
(2) / * |- / * - PR 
(3)/* h(/*)* -PR 



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As a second example, we will show that r+s = s+r, 
To do this,we have to show two things. 

(a) (r+s) D (s+r) 

(b) (s+r) D (r+s) 

We have a proof of (a) below: 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol.8,No.3,2010 

new rules which introduce universal quantifications... 



:-s +£? _r±^ +£l 



r + sh s * r-hsh r 

r+shs+ r 



-R 



Regarding the performance of our algorithm, 
nondeterminism is present in several places of this 
algorithm. In particular, there is a choice concerning 
which way the text is split in the^goal. Hodas and 
Miller[3] dealt with the goal rs by using IO-model in 
which each goal is associated with its input resource 
and output resource. The idea used here is to delay 
this choice of splitting as much as possible. This 
observation leads to a more viable implementation. 

Our ultimate interest is in a procedure for 
carrying out computations of the kind described 
above. It is hoped that these techniques may lead to 
better algorithms. 



We have to proof of (b) below 



5 .Acknowledgment 



r\- r 



s + s 



s + r\-r 



s + rhs 



A 



s + r\-r-\- s 



-R 



This paper was supported by Dong- A University 
Research Fund. 



6.REFERENCES 



4.CONCLUSION 



[i] S.C. Kleene, Introduction to Metamathematics, 
North Holland,Amsterdam,1964. 



We have described an algorithm for testing 
equivalence of 0-free regular expressions. The 

advantage of this algorithm is that it directly 
captures the real essences regarding language 
equivalence. As a consequence, it extends easily to 
other larger language classes such as context-free 
languages. For example, our algorithm extends 
easily to the one that deals with algebraic laws, i.e., 
regular expressions with variables. Two regular 
expressions with variables are equivalent if whatever 
expressions we substitute for the variables, the result 

are equivalent. For example,VZVM(Z+M -M +L). 

Algebraic laws are a useful tool for simplifying 
regular expressions. To deal with algebraic laws, it 
is a simple matter to extend our algorithm with some 



[2] J.Y. Girard, "Linear 
Computer Science,vol.50,pp 



logic". Theoretical 
.f-10i,1987. 



[3] J.Hodas and D. Miller "Logic programming in a 
fragment of intuitionistic linear logic " Journal 
of Information and Computation, r992. Invited 
to a speical issue of submission to the 1991 
LICS conference. 

[4] J.E.Hopcroft, R.Motwani, and J.D. Ullman, 
Automata Theory.Languages and Computation, 
Addison Wesley, zOOo: 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 



CRS, a Novel Ensemble Construction Methodology 



Navid Kardan 

Computer Engineering Dep. 

IUST 

Tehran, Iran 

n kardan@comp.iust.ac.ir 



Morteza Analoui 

Computer Engineering Dep. 

IUST 

Tehran, Iran 

analoui @iust.ac.ir 



Abstract — Constructing ensemble classifiers that are both 
accurate and diverse is an important issue of research and 
challenging task in machine learning. In this paper, we proposed 
Class-based Random Subspace (CRS) method; a new ensemble 
construction method based on the random subspace (RS) 
strategy, and tested it on a number of standard data sets from 
UCI machine learning repository. Our results show that CRS is 
at least as good as RS, and outperforms it in datasets with strong 
correlation between their classes. 

Random subspace method; Feature selection; Classifier 
ensemble; Classification 



I. 



Introduction 



The general methods for constructing ensemble classifiers 
can be applied to any classification algorithm. These methods 
try to create feature spaces that are as diverse as possible, from 
the original data set. The most successful of these methods are 
Boosting [1], Bagging [2], Random subspace [3], and decorate 
[4]. Another possibly promising approach in this area is the 
nonlinear transformations of the original data set [5]. 

In Boosting, every data instance is picked with a probability 
according to its hardness or misclassification on earlier 
classifiers of the ensemble. This way the harder instances that 
need more effort can gain more notification and we can get 
more diverse ensembles. 

In bagging, we make new data sets by sampling with 
replacement from the original data set. This way we have new 
data sets that have enough data points and introduce some 
degree of diversity in them, as well. 

In decorate, some artificial instances are added to the 
original data set for training a new member. The class label of 
these new instances is set with a value that is different from 
current ensemble decision. This way we strive for a better 
community with increasing diversity. 

The random subspace method samples features of the 
original data set, in order to improve diversity, instead of 
sampling from instances. This way, we have new data sets that 
have the same amount of data as the original, therefore, can get 
better classification accuracies. In some applications, such as 
biometric data processing, this method has proven very good 
performance [6]. In this paper we improve this approach by 
taking into account the classes that data items come from. We 



show that an ensemble created this way, permits to obtain 
better accuracies than that obtained by a classic RS method. 

II. Methods 

An ensemble of classifiers combines different learners in 
order to obtain a better overall performance. These methods 
build new data sets from the original data and build the 
classifiers on these modified data sets. Then the final decision 
is obtained by combining all of the individual votes. 

Suppose a data set with N instances and F features. In the 
original RS method we build K new data sets, where K is the 
number of base classifiers in the ensemble, by sampling 
randomly from the F features and obtain K new training sets 
each one with N training instances. Then we train each learner 
with one of these training sets. 

The main idea behind this method is the possible 
redundancy in the original feature space. With sampling from 
this space we select a subset of the base space and so it is likely 
to find a space that can lead to better generalization. This 
improvement in accuracy is obtained from combining these 
different projections of the feature space. Another advantage of 
this method is the reduced feature space that can tackle the 
problem of curse of dimensionality. In fact, Vapnic [7] showed 
that reducing the feature space is one method for gaining better 
generalization ability. 

Ho in [3] showed that RS can improve accuracy of the tree 
classifiers considerably, by building a random forest. 

RS method treats all features the same. It is also a top-down 
approach, i.e. it selects the new features at the start of training 
phase but it does not consider each instance individually. We 
compensated the original idea of RS according to the fact that 
the redundancy of data set can be class-based. This means, 
although there might be better subsets of the feature space that 
can lead to better classification, but if we consider the data as a 
whole, we might lose some of these redundancies that are 
between some parts of the original feature space. These parts of 
feature space, in our work, correspond to the different classes 
of the original data set. 

For having a better understanding of the situation, consider 
the case that we have three classes; say A, B, C in the data set. 
Class A can be best discriminated from other classes with 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 



feature subset As and the same is true for classes B and C. in 
the RS method, we suppose that A s , B s and C s are identical and 
we can get better results by selecting the subsets that can better 
approximate the best subset. But in the broader case when these 
subsets are different, we can get stuck in the situation that leads 
to no better performance. By taking into account this difference 
between feature subsets we can get better ensembles and higher 
accuracies. 

Fig. 1 shows a data set with five features and three class 
types that is sorted according to the class label. 













B 












Class 1 












Class 1 












Class 1 












Class 2 












Class 2 












Class 2 












Class 3 












Class 3 












Class 3 



Fig. 1. A sample data set with five features and three class 
types. 

In figure 2 one possible data set according to RS strategy is 
presented. In this figure the omitted feature is shown by /// 
mark. Figure 3 present one possible feature subset according to 
our method. As depicted in this figure, here we use different 
feature subsets for each class. 

Based on this idea we introduce the idea of Class-based 
Random subspace (CRS) that can be used to build classifier 
ensembles that are more likely to have better performance. In 
CRS every classifier is responsible for one class only and the 
final decision is obtained by combining different decisions 
according to their corresponding class. 

One important issue about our strategy is the way that we 
make it possible to have different subsets for different classes. 
Let we name a specific feature subset as /, feature space as F 
and number of class types as k. in our method, for each 
ensemble member we create k sub-feature sampling i.e. 
m={fij 2 > ••• > /*}• Each sub-feature/ will be used to train a 
classifier that identifies one class. According to this one-vs.-all 
strategy [8] we will make k classifiers for each unit of our 
ensemble. If our ensemble consists of 1 members, M = {m h m 2 , 
..., m/}, we will need kxl random samples from F. In test 
phase we will count the votes for each class and select the most 
popular class. 













Ill 




III 




Class 1 


III 




III 




Class 1 


III 




III 




Class 1 


III 




III 




Class 2 


III 




III 




Class 2 


III 




III 




Class 2 


III 




III 




Class 3 


III 




III 




Class 3 


III 




III 




Class 3 



Fig. 2. Selecting three random features to construct a new 
data set according to RS strategy 





III 




III 




Class 1 




III 




III 




Class 1 




III 




III 




Class 1 


III 








III 


Class 2 


III 








III 


Class 2 


III 








III 


Class 2 






III 


III 




Class 3 






III 


III 




Class 3 






III 


III 




Class 3 



Fig. 3. Selecting three random features to construct a new 
data set according to our strategy 



Our algorithm can be described as follows: 

• Build k data sets from the original data set, where k is 
the number of classes and each set is built for one of these 
classes; i.e. each set is exactly the original data set except for 
the class label that can only get two values, one for belonging 
to the corresponding class and another for belonging to other 
classes. 

• Using each of these new data sets, choose a subset of 
features randomly and project each instance to this space to 
build a new data set for each base classifier. 

• Train each classifier with its training data and 
remember its corresponding class. 

• For classification of a new instance, any combining 
method can be used to obtain the final decision of the 
ensemble. For instance, majority vote can add any vote to its 
corresponding class and choose the class with most votes. 



III. Experiments 

We used 5 datasets from UCI machine 
[9, 10] to evaluate our proposed method, 
method with the original RS method for 
sizes and the results show that our method 
that are at least as good as the RS 
improvements in some cases. 



learning repository 
We compared our 
different ensemble 
leads to ensembles 
and show major 



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We used Vehicle, Vowel, Soybean, Glass and DNA 
datasets as our test benchmarks. Vehicle dataset has 846 
instances and 19 features including class label. It is used for 
identifying four types of vehicles from their silhouette. Vowel 
dataset consists of 990 instances, each with 10 features. It has 
11 class types that are the steady state vowels of British 
English. Soybean dataset has 683 instances with 35 features for 
each and a class label which may have any of the 19 different 
values. Glass dataset has 214 observations of the chemical 
analysis of 7 different kinds of glass. It has 10 features 
including class label. DNA data set has 181 features including 
class label and has 3186 instances. 

All the experiments are done in R software. Our base 
classifier is the tree classifier implemented in the tree package 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
of this software [11]. For combining decisions of ensemble 
members, we used SUM rule [12]. This method computes sum 
of the decisions of ensemble members for each class and select 
the class with most value. 



For estimating testing error, we used leaving-one-out 
method [13]. We repeated this procedure for thirty times and 
averaged the results. Tables I-IV show statistical properties of 
the number of correctly classified instances for each data set. 
Each column represents the result of one method for a 
particular ensemble size. The ensemble size is written in 
parentheses. Table V represents statistical properties of the two 
ensemble classification accuracies on the DNA data set. 



TABLE I. 



Comparison of RS and CRS on Glass dataset using number of instances that classified correctly 



TABLE II. 



TABLE III. 



TABLE IV. 



Glass 


CRS(5) 


RS(5) 


CRS(IO) 


RS(10) 


CRS(15) 


RS(15) 


CRS(20) 


RS(20) 


Mean 


156.9 


152.93 


159.17 


152.13 


160.27 


151.83 


160.43 


150.77 


MAX 


168 


163 


167 


162 


167 


162 


165 


156 


MIN 


150 


144 


152 


146 


150 


142 


154 


143 


Std. Dev. 


4.41 


4.62 


3.33 


4.83 


3.62 


4.92 


2.91 


3.57 



Comparison of RS and CRS on Soybean dataset using number of instances that classified correctly 



Soybean 


CRS(5) 


RS(5) 


CRS(10) 


RS(10) 


CRS(15) 


RS(15) 


CRS(20) 


RS(20) 


Mean 


522.2 


513.93 


522.4 


512.23 


525.47 


511.87 


521.97 


511.17 


MAX 


554 


542 


534 


522 


543 


521 


534 


514 


MIN 


508 


506 


512 


503 


511 


505 


510 


504 


Std. Dev. 


11.49 


6.90 


6.51 


2.88 


8.26 


3.05 


6.54 


2.69 



Comparison of RS and CRS on Vehicle dataset using number of instances that classified correctly 



Vehicle 


CRS(5) 


RS(5) 


CRS(10) 


RS(10) 


CRS(15) 


RS(15) 


CRS(20) 


RS(20) 


Mean 


614.6 


614.47 


618.6 


616.17 


619.87 


619.03 


623.7 


618.53 


MAX 


638 


626 


630 


626 


632 


629 


636 


628 


MIN 


594 


599 


606 


600 


605 


600 


609 


610 


Std. Dev. 


13.01 


7.27 


7.57 


7.87 


6.18 


6.85 


5.26 


4.99 



Comparison of RS and CRS on Vowel dataset using number of instances that classified correctly 



Vowel 


CRS(5) 


RS(5) 


CRS(10) 


RS(10) 


CRS(15) 


RS(15) 


CRS(20) 


RS(20) 


Mean 


788.13 


558.23 


812.1 


574.53 


819.37 


578.5 


825.67 


578.8 


MAX 


809 


605 


834 


662 


833 


634 


843 


626 


MIN 


764 


499 


788 


525 


800 


524 


814 


532 


Std. Dev. 


12.14 


30.63 


11.54 


29.53 


9.10 


24.58 


7.55 


21.33 



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TABLE V. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
Comparison of RS and CRSonDNAdataset using classification accuracy 



DNA 


CRS(5) 


RS(5) 


CRS(10) 


RS(10) 


CRS(15) 


RS(15) 


CRS(20) 


RS(20) 


Mean 


0.9341 


0.9060 


0.9393 


0.9086 


0.9376 


0.9087 


0.9386 


0.9111 


MAX 


0.9523 


0.9303 


0.9517 


0.9222 


0.9504 


0.9253 


0.9523 


0.9297 


MIN 


0.9134 


0.8901 


0.9259 


0.8901 


0.9165 


0.8769 


0.9259 


0.8883 


Std. Dev. 


0.0090 


0.0101 


0.0067 


0.0096 


0.0094 


0.0107 


0.0062 


0.0096 



Figures 4-8 show the average ensemble accuracy vs. 
ensemble size for each data set. Here we can see that CRS was 
always at least as good as RS. In Vowel data set, CRS performs 
much better. In soybean glass and DNA it is considerably 
better. And in vehicle data set, two methods are not so 
different. 




-CRS 



10 



15 



20 



Fig. 4. The two methods are compared on Vehicle dataset using 

ACCURACY VS. ENSEMBLE SIZE 



0775 



LL/t> 

0.755 

0.75 

0.745 

0,74 
7Vj> 



■RS 
>CR5 



Fig. 6. The two methods are compared on Soybean dataset 
using accuracy vs. ensemble size 



[}.y 



D.76 



.,'..o 
0.7 



-RS 



U./J 

0.72 
0.71 



0.69 

o.es 




-as 

-CRS 



i:j 



r, 



20 



Fig. 5. The two methods are compared on Glass dataset 
using accuracy vs. ensemble size 



3.55 
0.5 


w u 










r 


lfj 


1j 


r 

10 



Fig. 7. The two methods are compared on Vowel dataset 
using accuracy vs. ensemble size 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 



0.95 

0.94 

032 
091 

0.E9 

c.sa 



*CRS accuracy 
-RS accuracy 



10 



15 



20 



Fig. 8. The two methods are compared on DNA dataset 
using accuracy vs. ensemble size 

IV. Conclusion 

We proposed a new ensemble construction strategy based 
on Random subspace method. In this method we tried to 
compensate classic RS method by trying to reduce the 
redundancy that might rise from intra-class correlations. If we 
have a brief comparison with the RS method and the fact that 
there can be intra-class redundancy in feature space, we can 
conclude that CRS have the potential to improve overall feature 
space in the same way that RS can do it in an unsupervised 
manner. We tested our method on four standard data sets and 
compared it to the RS method. The results suggest that our 
method is at least as good as original RS method, but in some 
cases there is a major superiority. These results are according 
to the fact that in some data sets there is a stronger correlation 
between classes and this can lead to better results. 



References 



[I] Y. Freund, R.E. Schapire, "Experiments with a new boosting algorithm", 
Proceeding of the thirteenth international conference on Machine 
Learning Bari, Italy, July 3-6, 1996, 148-156. 

[2] L. Breiman, "Bagging predictors", machine learning, 24, 1996, 123-140. 
[3] T.K. Ho, "the random subspace method for constructing decision 
forests", IEEE Trans. Pattern Anal. Mach. Intell. 20 (8) (1998) 832-844. 

[4] P.Melville, R.J. Mooney, "creating diversity in ensembles using artificial 
data", in information fusion: Special issue on diversity in multiclassifier 
systems", vol. 6 (1), 2004, pp. 99-111. 

[5] NicoLas Garc'ia-Pedrajas, Cesar Garc'ia-Osorio, Colin Fyfe, 
"Nonlinear Boosting Projections for Ensemble Construction", Journal of 
Machine Learning Research 8 (2007) 1-33. 

[6] L. Nanni, A. Lumini, "An experimental comparison of ensemble of 
classifiers for biometric data", Neurocomp. 69 (2006) 1670-1673. 

[7] V. Vapnic, "the nature of statistical learning theory", springer-verlag, 
1995. 

[8] E. Alpaydin, "Introduction to machine learning", Second edition, MIT 
press, 2010. 

[9] D.J. Newman, S. Hettich, C.L. Blake, C.J. Merz, UCI Repository of 
machine learning databases 

rhttp://www.ics.uci.edu/~mlearn/MLRepository.html l. Irvine, CA: 
University of California, Department of Information and Computer 
Science (1998). 

[10] A. Frank, A. Asuncion, UCI Machine Learning Repository 
rhttp://archive.ics.uci.edu/ml l . Irvine, CA: University of California, 
School of Information and Computer Science (2010). 

[II] B. D. Ripley, "Pattern Recognition and Neural Networks", Cambridge 
University Press, Cambridge, 1996. 

[12] J. kittler, M. Hatef, R. Duin, J. Matas, "On combining classifiers", IEEE 
Trans. Pattern Anal. Mach. Intell. 20 (3) (1998) 226-239. 

[13] R.O. Duda, P.E. Hart, D.G. Stork, "Pattern Classification", second ed., 
Wiley, New York, 2000. 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 

Routing Optimization Technique Using M/M/l 
Queuing Model & Genetic Algorithm 

Madiha Sarfraz, M. Younus Javed, Muhammad Almas Anjum, Shaleeza Sohail 

Department of Computer Engineering 

College of Electrical & Mechanical Engineering 

Pakistan 

madya.khan@gmail.com, myjaved® ceme.edu. pk, almasanjum@yahoo.com, shaleezas@hotmail.com 



Abstract — Optimization Approaches have been applied to 
various real life issues in communication and networking. In this 
research a new approach has been proposed for network path 
optimization using Genetic Algorithm. The path which is best 
fitted in the population is considered as the optimal path. It is 
obtained after qualifying the fitness function measuring criteria. 
The fitness function measures the best fitted path based on 
constraints; bandwidth, delay, link utilization and hop count. 
Population is composition of valid and invalid paths. The length 
of the chromosome is variable. So the algorithm executes 
competently in all scenarios. In this paper the comparison of this 
approach with the fitness function; measuring delay and 
bandwidth factor, has also been catered. This work has been 
performed on smaller network; work is in progress on large 
network. Thus, the results proved our affirmation that proposed 
approach finds optimal path more proficiently than existing 
approaches. 

I. Introduction 

OPTIMIZATION in the field of Genetic algorithm is 
gaining massive magnitude. GA is globally used 
optimization technique [1] based on natural selection 
phenomenon [2]. It is considered as an important aspect in 
networking. From source node to destination node essential 
solution is optimization which is needed. The network traffic 
flow is mounting rapidly. So the balance in Quality of Service 
and Broadcast of traffic should be maintained. Increased in 
load of network traffic will cause the delay in traffic and affect 
the QoS as well. The routing problem scenarios can be 
resolved through optimization [3]. 

In the research the approach which has been introduced is of 
network optimization routing strategy using Genetic 
Algorithm. It involves bandwidth, delay and utilization 
constraints. All these will be in different catering in scenarios. 
It digs out the most optimal path from the population lot on the 
basis of Fitness Function. The fitness function selects the path 
which has less delay, less utilization factor, more bandwidth 
availability and less number of hops to be travel. The hop 
count is used as the decision making factor when there are 
more than one path with same strength. The chromosome 
represents the path and the population is showing collection of 



feasible and infeasible paths. Each chromosome has variable 
number of nodes. This routing strategy is not efficient and 
robust merely but it also congregates swiftly. 

The paper is formatted as: The related work is done under 
section II. Overview of Genetic Algorithm has been structured 
in section III. In this the outline of GA and its operators are 
explained. Section IV explains the optimization strategy for 
routing using GA. In this two fitness functions are compared. 
The proposed fitness function includes bandwidth, delay, 
utilization and hop count. It is compared with the fitness 
function which is catering bandwidth and delay factor. The 
results and analysis is illustrated in section V. 



II. Related Work 

Genetic Algorithm which is a versatile technique designed for 
optimization and searching network planning control in Anton 
Riedl [4] work. It is also in planning of integration of packet 
switched network. 

Yinzhen Li, Ruichun He, Yaohuang Guo [20] work on finding 
out optimal path with fixed length chromosomes. This is 
following priority-based mechanism. In this work Mitsuo. Gen 
technique's loophole has also been indicated. 

In Carlos A. Coello Coello's tutorial [26] multiple objectives 
optimization has been discussed. The pros and cons of these 
approaches are done by them. Also illustrate research done 
and their implications in the respective field. 

Introduction of key-based technology in optimization is done 
by Mitsuo Gen and Lin Lin [16]. They have combined 
different operators [16]. They high level of search paradigm 
leading to improvement in computational time and path 
optimization. 

Basela S. Hasan, Mohammad A. Khamees and Ashraf S. 
Hasan Mahmoud [24] used Heuristic mechanism for Genetic 
Algorithm. They have considered single source shortest path. 
For searching heuristic approach is used for crossing over also 



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called as recombination and mutation. 



In other work of Anton Riedl [19] title represents the research 
done is upon path optimization in traffic engineering scenario. 
It discusses the implications of network optimization and 
traffic engineering. It main focus is routing with multiple 
delay and bandwidth constraints for optimization. 

Andersson and Wallace [25] proposed a GA which is robust 
and requires few numbers of parameters. It main emphasis is 
how multiple objectives GA works out on real life scenarios. 

An approach has been developed for reducing congestion in 
the network [17] by M. Ericsson, M.G.C. Resende and 
P.M.Pardalos. 

Ramon Fabregat, Yezid Donoso, Benjamin Baran, Fernando 
Solano and Jose L. Marzo [23] presented traffic-engineering 
load balancing classification. They have not work on packet 
loss and other factors like backup paths. They introduced 
GMM model. 

Diverse and versatile genetic algorithm is proposed in the 
work of Norio Shimamoto, Atsushi Hiramatsu and Kimiyoshi 
Yamasaki [18]. In this approach last result of the iteration is 
used for the next generation. Performance level of the 
algorithm is good. 

Abdullah Konak, David W. Coit, Alice E. Smith [21] shows 
that investigation for solutions is the coherent response for 
multiple objectives. A real life scenario entails immediately 
multiple objectives for optimization. It also give overview of 
GA which are developed for multipurpose objectives and 
maintaining the diversity. They have introduced GR (Greedy 
Reduction) technique. In the case of worst scenario GR 
technique executes in the linear time of framework [22]. 

For bandwidth allocation Hong Pan and I. Y. Wang [15] 
proposed GA for optimization. In this the average delay 
network is lessened. Bandwidth can be reassigned again as per 
this algorithm. 



III. Overview Of Genetic Algorithm (GA) 

Genetic Algorithm is a search paradigm. It follows 
principles which are based on Darwin Theory of evolution. In 
this population data fights for survival and the 'fittest' one 
survives. This algorithm is mainly based on natural selection 
phenomenon. GA is introduced by John Holland [7] [5]. It is 
effective technique as its not only encountering mutation but 
also use crossing over technique (or genetic recombination) 
[10]. Crossing over technique improves the proficiency of 
algorithm for having the optimum outcome. Holland had a 
dual aim [11]: 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
• To improve the concept of understanding of natural 
selection process 



• To develop artificial structure having functionality 
analogous to natural system 

Genetic Algorithm has been broadly used for solving MOPs 
because it works on a population of solutions. [13]. Objective 
function plays an important role for obtaining optimum result. 
For having "Optimum" result does not mean that the result is 
"Maximum". It's the best and the most appropriate value as 
per the objective function criteria. Genetic algorithm is best 
for optimization and is useful in any state. 

A. Population 

The Genetic algorithm starts up with a set of solutions which 
is taken as a 'population' with the assumption that next 
generation will be better than the previous one. The cycle is 
terminated when the termination condition is satisfied. 

B. Operators ofGA 

The efficiency of Genetic algorithm is dependent on the way 
the operators are used, that constitutes Genetic algorithm 
course of action [9]. The GA operators are as follows [8] [4]: 

1) Selection & Reproduction: Chromosomes are selected 
according to their Objective Function (also called as fitness 
function). The ultimate node for chromosome survival is 
Objective Function. It works on Darwinian Theory for 
survival of the fittest. This is an artificial version of natural 
selection for survival. Chromosomes having higher fitness 
have greater likelihood of being into next generation. There 
are numerous methods for chromosome selection. For 
selection of chromosomes several methods are in practice. 

2) Crossover or Recombination: It is the distinguishing 
feature from other techniques of optimization. On selected 
parents the technique is applied as per their crossing-over 
mechanism. 

• 1 -point Crossing over 

• 2-point Crossing over 
Crossing over results into new off-springs. 

3) Mutation: In GA maneuvering mutation has secondary 
role [6]. It is required after crossing over segment because 
there is a probability of information loss at this stage [12]. It is 
done by flipping by bit as per requirement. The population 
diversity maintenance is purpose of mutation operator. 



C. Steps to follow in GA 

1) Population Generation: Generate population randomly 
of'n'. 

2) Fitness Function: Launch function for evaluation of 
fitness in population. 



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3) Applying Operators: Create new population by 
applying operators of GA until the new population is 
complete. At the end of iteration another generation is 
attained. The Rank Based Selection is applied. 

a) Selection: Parents selection is on the basis of their 
fitness function creteria. 

b) Recombination: Recombination also called as 
crossing over is than applied over the selected parents. It result 
into offsprings. 

c) Mutation: The resulting offsprings are than mutated. 
After that their fitness function is measured. On the basis of 
this value, their survival in the population is based. 

4) Terminating Condition: If the terminating condition is 
reached, the loop breaks and the best result is obtained. 

5) Resulting Generation: Newly generated population is 
used for further generation. 

IV. Proposed Optimization Routing Approach 

In this proposed optimization approach basic unit of a 
chromosome is gene. Genes constitute to form a chromosome, 
which in turn constitutes the population. Thus the 
chromosome in population is represented by string of number 
as in Figure 1 . 

Figure 1 . Genetic Representation of Path 

The gene represents the node while the chromosome 
represents the network path. Population is the collection of all 
possible paths. The chromosome 1-5-3-6 shows network path 
which is constituted by nodes. The first node is the source 
node while the last node is the destination node. The hop count 
in this will be: 

Hop Count = Chromosome Length - 1 

The length of the chromosome is variable. So the number of 
nodes in the path is directly proportional to number of hop the 
data has to travel. The chromosome which have source and 
destination same as defined for the path, they constitute the 
population lot. The rest unfeasible paths will be discarded after 
they are generated. From those feasible paths, the population 
is generated randomly. 

A. Strategies 

The proposed strategy is explained in Fitness Function II and 
is compared with Fitness Function I as well. The proposed 
strategy is more efficient as it is handling more constraints 
than the other approach [27]. 



B. 



Fitness Function I 



Vol 8, No. 3, 2010 



This fitness function is for finding the delay of the path using 
the delay and bandwidth constraints [27]. The algorithm is as 
follows: 

• It checks the bandwidth availability of link. 

• After that network delay of link is find out 

• Than delay average of path is calculated. 

All the paths are valid, as they have passed through the 
bandwidth availability check before calculating the average 
packet delay as [27]: 

AveragePacketDelay = 0-) 

no. of links 

The delay of the path is calculated as [27]: 



delay = ^ 



bw Available. 



DataSize 
The optimum path selection is based 



(2) 
mainly on the 
bandwidth and delay in this approach. Thus, if two or more 
paths have same fitness value than the path with less number 
of hops will be taken as the optimum path. Optimum path 
leads to a path which has more bandwidth, less delay and less 
number of hops. 
Now the proposed strategy involving is illustrated as follows: 



C. 



Fitness Function II 



The proposed strategy is founded on M/M/l queuing model 
[14] by using GA for Path Optimization. In our research it is 
used for handling Bandwidth, Utilization and Delay 
constraints for finding Optimum path. The fitness function is 
based mainly on (3). If there is a conflict in making decision 
among these constraints than path with lesser hops is taken as 
the optimum path. Constraints defined are Bandwidth 
Availability, Delay, Utilization and Hop count. The fitness 
function using M/M/l queuing model [14] is as follows: 



T = 



1-p 



(3) 

T = Delay mean (sec) 

l/(i = mean packet size (bits) 

C = capacity (bps) 

p = utilization factor 

1) Capacity 'C: It is the bandwidth C available over the 
network path. 

2) Utilization factor 'p': After checking of bandwidth 
availability, the network utilization factor is obtained as: 



utili, = 



DataSize 
bwAvailable, 



(4) 



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After summing up of utilization factor a links, utilization 
factor value for a single path is obtained. Than mean of the 
utilization factor of path is obtained by dividing the sum of the 
utilization factor value for a single path with the total number 
of links in that path as: 

n 

Mean Utilization = (E Utilization) /total number of links 

(5) 

3) Delay Mean: The equation (3) is precisely formulized 
as follows: 

Delay Utilization Mean = (Mean Utilization)/ (1- Mean 
Utilization) 

(6) 
It constitutes fitness function of this algorithm: 



Fitness Function 



Delay Utilization Mean 

(7) 



This utilization factor is calculated for every path. After each 
generation the mean of all paths is also calculated. All the 
paths are valid path as their bandwidth availability is checked 
before finding out their fitness. Optimum path selection is 
based on the link utilization, Bandwidth, Delay Factor and hop 
count. The more the bandwidth, the less the delay, utilization 
and the hop count the more optimum the path will be. When 
there are two or more paths with same fitness value than hop 
count will be playing the decision making role. 

D. Proposed GA for Path Optimization 
Proposed Routing Strategy using GA for network path 
optimization is as follows: 



Vol. 8, No. 3, 2010 
applied on the selected chromosomes, leading towards the 

segregation of source and destination node. After this 1 -point 

crossing over technique is done over the resultant 

chromosome. In this the crossing over point is 2 [27]. 

4) Mutation: After crossing over, mutation is being done 
on the offsprings. Mutation has been done as per the scenarios 
which are [27]: 

a) Scenario 1 (repeating node): 

• The location of repeating node is traced out. 

• Any of the missing nodes is find out. 

• Place the missing node at traced location. 

b) Scenario 2 (no missing or repeating node): 

• Randomly pick two nodes from chromosome 

• Swap those nodes 

c) Scenario 3 (minimum chromosome length): 

• Minimum length of chromosome is 2 

• It has only source and destination nodes 

• There will be no mutation 

d) Scenario 4 (length of chromosome is one more than 
the minimum): 

• Length of chromosome one more than minimum 
constitutes of 3 nodes 

• Only middle node is flipped with missing node 

5) Evaluation of Mutated Offsprings: The mutated 
offspring's fitness is evaluated. The chromosome with worst 
fitness is replaced by the offspring having better fitness. The 
population size will remain unaffected by this replacement. 
The worst chromosomes are discarded. The network path 
survival is based on their Fitness Function Criteria. 



1) Initialization of Population /The population is randomly 
generated [27]. There also exists the probability of valid and 
invalid paths. The source and destination nodes are fixed. The 
randomly selected population is 33% of the generated 
chromosomes. So this 33% constitutes the whole population 
and 30 generations are produced [27]. 



V. Experiments, Results & Analysis 

A. Experiments and Testbed 

1) Network Formation: The delay, bandwidth and 
utilization factor has been evaluated on the following network: 



2) Selection of Parents: Parent's selection is based on 
Rank-based Selection. In this the parents are ranked on the 
basis of their fitness function value. They are not selected 
randomly except the first generation (in which parents are 
selected randomly). The parents with high fitness will be 
higher in ranking and the ones with low fitness will be lower 
in ranking. Both parents which are best in the population are 
used for crossing over as they have more tendency of survival 
in nature. 

3) Recombination (or Crossing Over): Crossing over of 
the best fitted chromosome results into best offsprings. The 
crossing over technique used is "2 -point over 1 -point 
crossover" [27]. In this first 2-point crossing over technique is 




Figure 2 . Network Formation 



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(IJCSIS) International Journal of Computer Science and Information Security, 



Main factors of algorithm are availability of bandwidth, 
utilization, delay and hop count in network traffic. When the 
packet is passed through a network, two parameters are 
involved. One is time packet takes to reach next node and 
other is bandwidth. Time also shows distance packet travels. 
The variety of time and bandwidth measures is included in the 
network, leading to better result in a complicated network. 

2) Population Selection Criteria: The population is 
selected randomly. The result of randomly selected 
population's result is compared with whole population and 
with results of the [15] also. The obtained results are better 
than both of them. Hence, proved that random selection is best 
choice [27]. The criterion for population size and population 
generation is as shown in the following table [27]: 



TABLE I. 



TABLE SHOWING POPULATION SIZE & NUMBER OF 
GENERATIONS 



Population Size 


33% 


Generations 


30 



3) Experimental Practice: The population is selected 
randomly. First bandwidth availability is checked and then 
calculates utilization and delay factor as mentioned in fitness 
function. The hop count is being considered for resolving the 
conflict between paths with same fitness value. The paths with 
less hop count in that case will be considered as an optimum 
path. Even if the hop count is same, than select any of the path 
which is not used often. 

B. Analysis & Results 

Fitness I is applied over the whole population. The population 
is generated 50 times. The minimum average Delay is taken 
till 50 iterations and is shown in the graph below. So, there 
will be a delay in getting the optimum result. 
The horizontal axis is showing the generation and the vertical 
axis is showing the delay average (fitness function value) 
against every generation. 



delay avg full popu 



1.4 
1.2 

1 
0.3 
0.6 
0.4 
0.2 





-delay avg full popu 



1 4 7 10 13 1619 22 25 23 3 1 34 37 40 43 46 49 



Figure 3. 



Vol 8, No. 3, 2010 
Average Delay (whole population) 



In this case number of hops is constant. There is no alteration 
in hop count till the 50 generation. It might show change later. 
The graph is constant showing no variation. 

Now the population is randomly selected and fitness function I 
is implemented over it. The results are compared with the full 
population selection results. The 33% of the population is 
taken and is generated 30 times as per Table I. The minimum 
of delay average of every generation is shown in the Figure 4. 
Hence, it is showing better results than shown in the Figure 3. 
There is a variation in this graph and is reaching the minimum 
level of delay. 

The horizontal axis is showing the generation and the vertical 
axis is showing the delay average (fitness function value) 
against every generation with random selection of population. 



DelayAvg 




0,95 



0,35 



13 5 7 9 1113 15 17 19 2123 25 27 29 3133 35 37 39 4143 45 47 49 51 



•DelayAvg 



Figure 4. Average Delay (randomly selected population) 

As per the results obtained the number of hops is also 
reducing. The hops are more at the start of the generation, but 
as the generations are increasing their number of hops is also 
reducing. Therefore, it is involving traffic engineering of the 
network as well. So in this case it is sharing the load of 
network through delay and bandwidth constraints. In this 
utilization factor is not involved in fitness function criteria. 

At start the hops count of the paths are more as per the 
network scenario, but in final result the number of hops is 
lesser. Thus, it is also handling the traffic engineering problem 
of the network. This is thus sharing and balancing the network 
load as per bandwidth and delay criteria. 

Now the proposed strategy (fitness function II) is applied over 
the network. Population selection is random [27]. It involves 
the delay, bandwidth and utilization constraints. It also 
involves hop count. 

The horizontal axis is showing the generation and the vertical 
axis is showing the delay and utilization measure (fitness 



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function II) against every generation with random selection of 
population. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
Figure 6 is showing comparison of the proposed algorithm 

with the other algorithm mentioned in this research. 



utilization n mean delay 




■utilization n mean 
delay 



1 4 7 1013161922252831343740434649 



Figure 5. Proposed Algorithm involving utlization and Delay constraints 
with randomly selected population 

In this graph there is varitaion in fitness value involving delay 
and utilization factors of the network. Thus it is reaching the 
minimum value more earlier than the previous algorthims 
discussed. It is reaching getting value below 1 at ealier stage 
which fitness function I graph is showing after mid generation. 

In Figure 6 the fitness function which is involving more 
constraints are showing better results than compared to the 
other fitness function results mentioned in this research paper. 
So for calculating the optimum path of the network the Fitness 
Function II should be applied over the network for congestion 
control and traffic engineering problems. 



1.4 




•DelayAvg 

■utilization n mean delay 

--delay avg full popu 



1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 



Figure 6 . Comparison of both fitness function and respective population 
selected (whole and random selection) 



VI. Conclusion 

Bandwidth scaling is elementary driver of popularity and 
growth among interconnected computer networks. Increase in 
bandwidth accompanies lesser delay. In this research, the 
proposed technique is for finding optimized path with delay, 
bandwidth and utilization measures. In this the comparison is 
done between the fitness function catering just bandwidth and 
delay with the fitness function handling bandwidth, delay and 
utilization. Both of them are tackling the hop count. The 
results prove our affirmation that proposed algorithm shows 
better results than the earlier proposed algorithms. 



References 

[I] David E. Goldberg, "Genetic Algorithms in Search, Optimization & 
Machine Learning", Addison-Wesley Longman Publishing Co., Inc. 
Boston, MA, USA, 1989. 

[2] A.W.W.NG and B.J.C. Perera, "Importance of Genetic Alrothim 
Operators in River Water Quality Model Parameter Optimisation", 
school of the Built Environment, Victoris University of Technology, 
Melbourne. 

[3] Zhao-Xia Wang, Zeng-Qiang Chen and Zhu-Zhi Yuan, "QoS routing 
optimization strategy using genetic algorithm in optical fiber 
communication networks", Journal of Computer Science and 
Technology, Volume 19 , Year of Publication: 2004, ISSN: 1000-9000, 
Pages: 213 -217 

[4] Anton Riedl, "A Versatile Genetic Algorithm for Network Planning", 
Institute of Communication Networks Munich University of 
Technology. 

[5] Darrell Whitley, "A genetic algorithm tutorial", Statistics and 
Computing, 1992, volume 4, number 2, pages 65-85. 

[6] David E. Goldberg, "Genetic Algorithms in Search, Optimization & 
Machine Learning", Addison-Wesley Longman Publishing Co., Inc. 
Boston, MA, USA, 1989. 

[7] Darrell Whitley, "A genetic algorithm tutorial", Statistics and 
Computing, 1992, volume 4, number 2, pages 65-85. 

[8] David E. Goldberg, "Genetic Algorithms in Search, Optimization & 
Machine Learning", Addison-Wesley Longman Publishing Co., Inc. 
Boston, MA, USA, 1989. 

[9] A.W.W.NG and B.J.C. Perera, "Importance of Genetic Alrothim 
Operators in River Water Quality Model Parameter Optimisation", 
school of the Built Environment, Victoris University of Technology, 
Melbourne. 

[10] Emmeche C, "Garden in the Machine. The Emerging Science of 
Artificial Life", Princeton University Press, 1994, pp. 114. 

[II] Goldberg D., "Genetic Algorithms", Addison Wesley, 1988. 

[12] David M. Tate, Alice E. Smith, "Expected Allele Coverage and the Role 
of Mutation in Genetic Algorithms", Proceedings of the 5th International 
Conference on Genetic Algorithms Pages: 31 - 37 Year of 
Publication: 1993, ISBN:l-55860-299-2 

[13] Dinesh Kumar, Y. S. Brar, and V. K. Banga, Multicast Optimization 
Techniques using Best Effort Genetic Algorithms, World Academy of 
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[14] Yantai Shu, Fei Xue, Zhigang Jin and Oliver Yang, " The Impact of 
Self-similar Traffic on Network Delay", Dept. of Computer Science, 
Tianjin University, Tianjin 300072, P.R. China, School of Information 
Technology and Engineering, University of Ottawa,Ottawa, Ontario 
Canada KIN 6N5, 0-7803-43 14-X/98/$10.0081998 IEEE 



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[15] Hong Pan, Irving Y. Wang, "The Bandwidth Allocation of ATM 
through GA", Global Telecommunications Conference, 1991. 
GLOBECOM '91, 1991, pages 125-129. 

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Problem by Random Key-based GA", Genetic And Evolutionary 
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weight setting problem in OSPF routing", Journal of Combinatorial 
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[18] Norio. Shimamoto, Atsushi Hiramatsu, Kimiyoshi Yamasaki, "A 
dynamic Routing Control based on a Genetic Algorithm", IEEE 
International Conference on Neural Networks, 1993, pages 1123-1128. 

[19] Anton Riedl, "A Hybrid Genetic Algorithm for Routing Optimization in 
IP Networks Utilizing Bandwidth and Delay Metrics", Institute of 
Communication Networks, Munich University of Technology, Munich, 
Germany. 

[20] Yinzhen Li, Ruichun He, Yaohuang Guo, "Faster Genetic Algorithm for 
Network Paths", Proceedings of The Sixth International Symposium on 
Operations Research & Its Applications (ISORA'06) Xinjiang, China, 
August 8-12, 2006. Pages 380-389. 

[21] Abdullah Konak, David W. Coit, Alice E. Smith, "Multi -objective 

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Jose L. Marzo, "Multi-objective optimization scheme for multicast 
flows: a survey, a model and a MOEA solution", Proceedings of the 3rd 
international IFIP/ACM Latin American conference on Networking, 
2006, pages 73-86. 

[24] Basela S. Hasan, Mohammad A. Khamees, Ashraf S. Hasan Mahmoud, 
"A Heuristic Genetic Algorithm for the Single Source Shortest Path 
Problem", IEEE/ACS International Conference on Computer Systems 
and Applications, 2007, pages 187-194. 

[25] Johan Andersson, "Applications of a Multi-Objective Genetic Algorithm 
to Engineering Design Problems", Department of Mechanical 
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[26] Carlos A. Coello Coello, "A Short Tutorial on Evolutionary 
Multiobjective Optimization" 

[27] Madiha Sarfraz, Younus Javed, Almas Anjum, Shaleeza Sohail, 
"Routing Optimization Strategy Using Genetic Algorithm Utilizing 
Bandwidth and Delay Metrics", 2010 the 2nd International Conference 
on Computer and Automation Engineering (ICCAE 2010), Singapore 



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Architectural Description of an Automated System for 
Uncertainty Issues Management in Information 

Security 



Haider Abbas 

Department of Electronic 

Systems, 

Royal Institute of 

Technology, Sweden 

haidera@kth.se 



Christer Magnusson 

Department of Computer and 

System Sciences, 

Stockholm University, 

Sweden 

cmagnus@dsv.su.se 



Abstract — Information technology evolves at a faster pace giving 
organizations a limited scope to comprehend and effectively react to 
steady flux nature of its progress. Consequently the rapid 
technological progression raises various concerns for the IT system 
of an organization i.e. existing hardware/software obsoleteness, 
uncertain system behavior, interoperability of various 
components/methods, sudden changes in IT security requirements 
and expiration of security evaluations. These issues are continuous 
and critical in their nature that create uncertainty in IT 
infrastructure and threaten the IT security measures of an 
organization. In this research, Options theory is devised to address 
uncertainty issues in IT security management and the concepts 
have been deployed/validated through real cases on SHS 
(Spridnings-och-H'dmtningssystem) and ESAM (E-Society) 
systems. AUMSIS (Automated Uncertainty Management System in 
Information Security) is the ultimate objective of this research 
which provides an automated system for uncertainty management 
in information security. The paper presents the architectural 
description of AUMSIS, its various components, information flow, 
storage and information processing details using options valuation 
technique. It also presents heterogeneous information retrieval 
problems and their solution. The architecture is validated with 
examples from SHS system. 



Keywords: Information Security, 
Options Theory 



Uncertainty Issues, 



I. Introduction 

Technological uncertainty due to rapid development and 
innovation in IT, continuously impacts security measures of an 
organization. The development is desirable that could facilitate 
business organizations with innovative hardware, novel 
methods and state of the art technologies. While on the other 
hand, technological progression also requires business 
organizations to adapt new methods and technologies to secure 
their information system (storage, retrieval, communication 



Louise Yngstrom 

Department of Computer 

and System Sciences, 

Stockholm University, 

Sweden 

louise@dsv.su.se 



Ahmed Hemani 

Department of Electronic 

Systems, 

Royal Institute of 

Technology, Sweden 

hemani@kth.se 



etc) processes. The objective could be achieved by deploying 
new security methods and by evaluating their validity, 
serviceability and interoperability using re-evaluation. But the 
service acquisition and validation process for IT security 
mechanisms is victimized by uncertainty due to new 
unforeseen threats and technological advancements appearing 
from time to time. Also these newly acquired security 
services/features may affect other interacting systems, this is 
referred to as externalities [1][2]. We addressed three major 
concerns in information security management due to 
technological uncertainty i.e. dynamically changing security 
requirements [3], IT security externalities [4] and obsoleteness 
of security evaluations [5]. We have utilized options theory 
from corporate finance [6]; known due to significance of 
providing effective guidance during uncertain investments. The 
options theory has been transformed using adaptability model 
[7] to tailor the IT security processes. The options theory 
methods were manually applied to illustrate and validate the 
concepts using real cases on ESAM (E-Society) [8] and SHS 
(Spridnings-och- Hamtningssystem) [9] systems. The ultimate 
objective of this research is to develop an automated solution 
(AUMSIS: Automated Uncertainty Management System in IT 
Security) for uncertainty issues management in IT security. 
The solution can be deployed in an organization and will be 
capable of providing system generated reports for; i) 
requirement change summary and suggested solutions ii) 
externalities report and internalization parameters and iii) re- 
evaluation strategy/guidance based on actual system state. In 
this paper, we will present the architectural description of the 
AUMSIS system which consists of its various components, 
architectural styles, information flow between components, 
storage details and heterogeneous information processing 
description. 

The paper is organized as follows: Next in section 2, the 
related work will be highlighted, section 3 presents the holistic 
view of the IT security uncertainty issues and section 4 



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presents the concept of automated uncertainty management 
solutions and elaborates its various constituents. Section 5 
describes the information processing and flow in AUMSIS. 
Section 6 elaborates heterogeneous information processing 
problem and the proposed solution for this issue. Section 7 
presents the discussion about the analysis and validation of the 
AUMSIS framework using SHS example. Section 8 presents 
conclusion and the future intention of this research. 

II. Related Work 

Automated information processing systems have been 
emphasized from various researchers in many domain areas. 
For example, Wilson, D. et al. has discussed various issues in 
automated inspection and representation of uncertainty for the 
real world issues [10]. McVicker, M. et al. has presented the 
infrastructure that collects statements of security-related 
statistics from the World Wide Web for source reliability 
verifications [11]. The work presented in this paper addresses 
the automated solution of uncertainty issues that might 
suddenly appear during IT security requirements/evaluation 
management and require a cumbersome solution exploration 
process with significant resources [12]. The ultimate outcome 
of this research will benefit organizations to have system- 
generated reports for IT security management i.e. i) changing 
requirements solutions ii) internalization guidance, iii) re- 
evaluation strategies and iv) security investment related 
suggestions/decisions. 

III. Information Security and Uncertainty Issues 

Most of the businesses today rely on IT infrastructures and 
have to deploy various security mechanisms to protect their 
work processes. Technological uncertainty strongly impacts 
those security mechanisms, which become obsolete with the 
rapid technological progression. The research emphasizes three 
critical concerns caused by technological uncertanity for an 
organization in IT security perspective as depicted in Figure 1 . 



Problems Caused by Technological Uncertainty 

1- Dynamically Changing Security Requirements 



2- IT Security System's Externalities 



3- Continuous Security Evaluation /Re-evaluation of IT 
products/Mechanisms 



An organization continuously has to go through a cumbersome 
procedure to deal with uncertainty issues and to keep their IT 
system up-to-date and according to new technological 
standards. The research aims for an infrastructure that will help 
to avoid the resource-hungry procedures and frame the system 
state, organizational needs, system's externalities issues and re- 
evaluation requirements analysis. The next section presents the 
architectural details of such an automated system (AUMSIS) 
that can be deployed in an organization. The system will 
automatically generate uncertainty solution reports for the 
issues depicted in Figure 1 . 

IV. Automated Uncertainty Management Solutions 
in Information Security 

AUMSIS is aimed to provide system-generated strategic 
guidance for above-mentioned issues described in section III. 
Decision-makers can use this information to formalize current 
and future IT security management strategies based on actual 
system state, which consists of organizational policies, up- 
coming technologies, vulnerability logs, attack history and 
available budget. Figure 2 depicts the abstract view of the 
AUMSIS architecture as follows: 



r Internet A 






Organizational 
Policies/ Budget 
Information 






w 



Up-coming 
Technologies 



Knowledgebase 





















Security 
System 
Vulnerability 
Reports 






Attack 
Histories 








Externality 
Reports 








Option 
Analysis 
Data 






Security 
Requirements 





Figure 1 . Uncertainty issues addressed in AUMSIS 



Figure 2. AUMSIS Architecture 

The various components of AUMSIS architecture depicted in 
Figure 2 are elaborated as follows: 



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A. Knowledgebase 

Information related to system state during a specified time 
period is named as historical data and organized in a 
structured repository; knowledgebase. It consists of following 
components: 

i) System vulnerability reports 

It contains malfunctioning reports of the security system and 

the corresponding affected security components. The 

information can be used to track the actual service/component 

causing vulnerability and provides details to determine system 

state. 

ii) Attack history 

Attack history data contains information about the 

exploitation of a particular security service/component by 

authorized/unauthorized sources. It will reveal shortcomings 

in existing security mechanisms that need to be factored in. 

iii) Externality reports 

IT security system of an organization may also leave positive 
or negative effects to other interacting systems/sub -systems 
referred to as externalities [1]. Externalities of a security 
system [2] can be identified by internal/external 
malfunctioning reports from affected systems/partners. 
Externality reports provide a holistic view of the IT security 
system and help to determine system's desired functionality. 

iv) Options analysis data 

AUMSIS generates results using options technique that are 
reusable by subsequent analysis. Options analysis data 
contains information about already executed options and 
results from a previous analysis. Option cards were used to 
store data about the options analysis outcomes [3]. 

v) Security Requirements 

Security requirement change reports from various 
stakeholders or security requirements from any external 
enforcing authority. This is continuously updated to factor in 
new/changed requirements. 

B. Up -coming Technology and Organizational 
policies/Budget information 

It is the prime objective of AUMSIS to provide contemporary 
guidance about requirement solutions, internalization factors 
and evaluation strategy. Therefore the AUMSIS has to interact 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 

and extract information from Internet and organization's 
policy database. These factors are considered as a separate 
component in AUMSIS due to their evolving nature during 
analysis. 

Above-mentioned historical data, information about 
upcoming-technologies and organizational policies/budget are 
accumulated over time and readily available for options 
analyst agent (OAA) for processing. 

C. Options Analyst Agent (OAA) 

Options analysis agent is a piece of software [13] that 

formalizes requirement solutions, internalization results and 

evaluation strategy using options technique. It extracts system 

state from knowledgebase, Internet (for up-coming 

technologies) and organizational policy/budget database. 

OAA generates the strategic information for decision makers 

i.e. 

i) Analyze alternative solutions for a security requirement and 

provides recommendations based on contemporary system 

state. 

ii) Internalizing solutions for externalities according to 

organizational policies. 

iii) Deterministic test plans strategies for the evaluation 

process of each security service considering its 

malfunctioning report and service exploitation history. 

AUMSIS provides up-to-date strategic guidance for the 
uncertainty issues in information security management 
process. It considers uncertainty elements caused by changing 
environment and helps to devise respective optimal IT 
security strategy. Next section describes the information 
processing and flow in AUMSIS system. 

V. AUMSIS Information processing and flow 

AUMSIS provides strategic guidance for three main areas of 
information security management affected by uncertainty 
issues. The uncertainty management process using AUSMIS 
for these issues follows slightly different mechanism due to 
the nature of problems. But the data are maintained in a single 
repository i.e. knowledgebase. As the AUMSIS addresses 
three uncertainty concerns in IT security, each one is 
elaborated individually in Module 1, Module 2 and Module 3. 
Figure 3 below depicts the information flow of these three 
modules using information flow diagram as follows: 



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Start J Vol. 8, No. 3, 2010 



Module 1 

Dynamic Requirements 
Management 



Module 2 

Externality 
Management 



Module 3 

Re-evaluation of 
Security System 



Identification 

Stakeholders/Requirements/lnternalization Parameters/Security system 



Formulation in Options Theory Context 
(Data from Knowledgebase) 



Options Analysis for alternative solutions 



Each Selected Solution 



Up-coming 

technologies/Budget 

information/Uncerta 

inty Revelation 




Organizational 

Strategy/ 

Uncertainty 

Revelation 



Test Results 

Interdependence/ 

Uncertainty 

Revelation 



Option Selection 
Opt/Delay/Abandon/Switch 





Figure 3. Information flow for Module 1, Module 2 and Module 3 



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i) Module 1: Information processing flow of dynamic 
security requirements management 

Dynamically changing security requirement management 
process starts with the identification of requirement change in 
an organization. It proceeds with examining all possible 
solutions for this particular requirement. Each solution is 
divided into parts and analyzed/compared with system state 
(determined by the data from knowledgebase), organizational 
policies/budget and up-coming technologies. The significance 
of the approach followed by AUMSIS for solution exploration 
is the options theory. That concurrently analyzes all solutions 
and decides about each solution to delay, abandon or opt in 
existing scenario. It provides decision makers analysis reports 
for the requirement under-consideration, its possible solutions 
and the pros and cons of each solution according to their 
organization's information system state. 

ii) Module 2: Externality management information 
processing and flow 

AUMSIS generates internalization recommendations for the 
externalities caused by a security system. The security system 
is already described in knowledgebase according to security 
mechanisms/services it offers. Internalization process starts 
with identifying externalities by analyzing system data (from 
knowledgebase). The next phase is to list all possible solutions 
(internalization parameters) according to organizational 
policies and available budget/resources. Each solution is then 
divided into parts and analyzed using options technique to 
build organization's internalization strategy considering 
current system state and organization's future plans. AUMSIS 
generates internalization results for each internalization 
parameter to delay, opt and abandon according to existing 
scenarios as depicted in Figure 3. 

iii) Module 3: Re-evaluation of security services 
/mechanisms information processing and flow 

AUMSIS helps to build re-evaluation strategy for IT security 
services/mechanisms considering the uncertain factors i.e. 
requirements/polices change, vulnerability appearance and 
interoperability issues that adversely impact evaluation 
process. The process starts with identifying the boundaries of 
system for evaluation. It could be the newly adapted solutions 



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Vol. 8, No. 3, 2010 

in a security system or already evaluated components that 
need to be re-evaluated. Tests are classified according to the 
nature of the system under consideration. Uncertainty issues 
during re-evaluation are dealt using options technique in 
AUMSIS as tests are prioritized based on pervious evaluation 
results and vulnerability reports from knowledgebase. 

VI. HETEROGENEOUS INFORMATION ISSUE 

As the AUMSIS retrieves information from various 
information sources (i.e. knowledgebase, Internet, 
organization's policies database) and therefore varies in their 
structure, syntax and semantics. It is not directly 
comprehendible by the Option Analyst Agent (OAA). 
Therefore it is desirable to store information in uniformly 
accessible and extractable manner. Without considering the 
operating systems used and the hardware running these 
softwares. To overcome the issue of heterogeneous 
information retrieval we have proposed the use of ontologies 
[14] [15] that provide a shared conceptualization of a system or 
domain. The language used will be Web Ontology Language 
(OWL) for the development of ontologies. Which is based on 
strong constructs of description logic and is thus useful to 
represent any set of rules that are options concepts, 
organizational policies, internalization parameters etc in case 
of AUMSIS. 

With the help of the options analyst agent these ontologies can 
be traversed to find the useful information models and to 
resolve the semantic heterogeneity issues in AUSMIS 
components. These issues are raised due to the merger of 
information from various domains i.e. policy database, 
technological information and vulnerability/malfunctioning 
reports. It is worth mentioning here that the knowledgebase 
contains all the organizational polices and rules. This 
information plays a key role when OAA accesses information 
from various information sources and formalizes 
decisions/strategy. Figure 4 depicts the heterogeneous 
information retrieval framework as follows: 



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Information Source 1 Information source 2 Information source n 



Domain Ontology Domain Ontology Domain Ontology 

Resolving semantic 
eterogeneity 




Retrieve information 
processing rulesc^ 



Option analyst agent 



Figure 4. Option analyst agent's communication with ontologies and 
knowledgebase 

VII. Aumsis Analysis and Validation 

AUMSIS architecture presented in previous sections is based 
on an in-depth study of its methodological details and manual 
deployment to SHS and ESAM system in past [2] [3] [4]. The 
current AUMSIS design/information flow is about the 
automated version of options technique's concept for 
uncertainty management in information security. The 
architecture currently addresses three main uncertainty issues 
but is flexible to opt any other problem's mechanism for 
uncertainty management in information security. Example 
given below presents the SHS uncertainty management 
process using AUMSIS in a nutshell. It is worth mentioning 
here that AUMSIS will be deployed into the organization that 
interacts and extracts required information about the target 
system i.e. SHS in this example. 

A) Changed requirement request 

The process is initiated when a change requirement is 
identified for SHS system. This could be initiated by an 
internal source (stakeholders, management and implicit 
system's request) or by some external source (government 
enforcing authority) to adapt new standards. Once a change 
request is identified; it acts as a stimulus to AUMSIS process 
for the SHS system. 



i) Data collection 

AUMSIS decides about the optimal solution for a requirement 
change based on actual system state and within existing 
circumstances. Knowledgebase provides data to determine 
system state using vulnerability/malfunctioning reports and 
system exploitation history with respect to affected SHS 
services. Dynamic factors like uncertainty revelation, budget 
information and up-coming technology information are also 
continuously accessed/considered in solutions formulation 
process as described in next section. 

ii) Options analysis 

OAA retrieves information about input data of requirement 
change for SHS and lists all available solutions for the certain 
requirement. Each solution is then assigned priorities 
determined by the up-coming technology, budget information 
and uncertainty involved in current state. Options theory 
provides various alternatives to opt, delay or abandon a 
solution based on uncertainty revelation; also during solution 
formulation process. AUMSIS analyzes each possible solution 
by staging its deployment process and wait for additional 
information that becomes available with the time during 
exploration and analysis. This additional information normally 
requires altering the requirement selection strategy; and this 
facility is factored in as a core feature of AUMSIS. Thus it 
provides optimal solution about a requirement considering all 
possible factors that cause uncertainty in determining a 
solution. The output information of a solution evolution 
process is stored in knowledgebase that can be used later and 
provides guidance to examine future strategy. 

The newly opted solutions for SHS from a requirement 
management process may cause positive or negative effects 
for other interacting partners. Next phase elaborates how these 
effects are addressed as externalities in AUMSIS. 

B) Externality management process 

Externalities are the effects borne by the systems that are not 
involved in a direct communication with the SHS security 
system. These effects could be positive (that might bring in 
benefits) or negative (that might cause vulnerabilities) and 
may appear anytime during the life cycle of SHS system. 
AUMSIS initiates externality management process by 



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specifying internalization parameters that describes solutions 
in case of externality occurrences due to SHS system. 
Organizations (responsible for controlling security system) 
specify possible internalization parameters according to their 
security objectives and are stored in knowledgebase. 

i) Options Analysis 

When an externality is reported/detected for SHS either 
positive or negative, OAA lists possible internalization 
parameters for each externality and compares with 
organizational constraints, which include budget information 
and organizational policies. These factors are uncertain that 
may change and affect externality management process. It is 
also uncertain if a solution will work appropriately. OAA 
stages each solution into sections and analyzes them 
individually. All solutions for the externalities of SHS are 
decided using various options to delay, abandon or alter 
decision with respect to uncertainty revelation, rational 
analysis, budget and organizational policies. These factors can 
be determined using the data from knowledgebase. AUMSIS 
mechanism of externality management helps to 
deterministically consider variable factors and to respond 
accordingly for a specific scenario. 

System up-gradation in case of newly installed services for 
requirement management or externalities solutions (that 
recommended modifications) requires to re-evaluate the 
security system to test individual functionality and as a whole 
interoperability. This factor is also addressed in AUMSIS as a 
part of a complete solution and described in following section: 

C) Initiation of Re-evaluation process 

Re-evaluation is performed particularly when new solutions 
are devised. For example in case of SHS system when the 
existing system was reconfigured/modified. It is also 
recommended as periodically scheduled analysis for the 
complete system. AUMSIS classifies evaluation tests into two 
major categories i.e. assurance and criticality [7]. Assurance 
class contains tests to validate performance, serviceability and 
functionality. Criticality class contains tests that may alter 
testing strategy and are directly affected by uncertain 
outcomes/uncertainty issues those are interoperability, 
technological innovation and budget. 



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Vol. 8, No. 3, 2010 

i) Options Analysis 

OAA customizes and organizes evaluation strategy for a 
particular service of SHS based on its history of service 
failure, vulnerability reports and exploitation history. The 
information is extracted from the stored data from 
knowledgebase; which becomes readily available to OAA. 
Tests are prioritized based on this information and system 
state. AUMSIS using options theory; provides a deterministic 
approach to generate evaluation strategy and the ability to 
alter the evaluation directions. It helps to avoid unnecessary 
tests that can be determined by the information from uncertain 
outcomes and uncertainty revelations. 

VIII. Conclusion & Future Work 

Organizations need to overcome uncertainty issues in their 
information security management progress due to obvious fact 
of rapid technological development. They continuously 
require significant changes in their existing security 
infrastructure to meet the organizational security objectives 
and security standards. Organizations also have to invest huge 
resources and have to go through a cumbersome procedure to 
keep their system up-to-date. The paper introduced AUMSIS, 
the infrastructure of an automated system for uncertainty 
management issues at organizational level based on an in- 
depth study and manual validation of these concepts in past. 
The system is capable of managing dynamic issues using 
options theory mechanism from corporate finance that helps to 
generate appropriate strategies according to system state. The 
paper presented the architectural details and information flow 
for AUMSIS system and its various components. The future 
intention of this research is the deployment of AUMSIS 
framework into a software architecture style. 

References 

[1] Richard Cornes, Todd Sandler, "The Theory of Externalities, Public Goods and 
Club Goods", Cambridge University Press, June 1996 

[2] Ann Cavoukian, "Privacy as a Negative Externality The Solution: Privacy by 
Design" Workshop on the Economics of Information Security, London, June 24, 
2009 

[3] Abbas Haider, Yngstrom Louise and Hemani Ahmed, "Empowering Security 
Evaluation of IT Products with Options Theory", in 30th IEEE Symposium on 
Security and Privacy 2009, Oakland, California, USA 



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[4] Abbas Haider, Magnusson Christer, Yngstrom Louise and Hemani Ahmed, "A 
Structured Approach for Internalizing Externalities Caused by IT Security 
Mechanisms", In Proceesdings of IEEE International Workshop on Education 
Technology and Computer Science (ETCS 2010), March 2010, Wuhan, China 

[5] Abbas Haider, Yngstrom Louise and Hemani Ahmed, "Option Based 
Evaluation: Security Evaluation of IT Products Based on Options Theory", In 
Proceddings of IEEE Eastern European Regional Conference on the Engineering 
of Computer Based Systems 2009, Novi Sad, Serbia, Pages.134-141 

[6] J. Mun, "Real Options Analysis - Tools and Techniques for Valuing 
Strategic Investments and decisions", Wiley, Finance, 2002 

[7] Abbas Haider, Yngstrom Louise and Hemani Ahmed, (2009), "Adaptability 
Model Development for IT Security Evaluation Based On Options Theory" in 
proceedings of IEEE/ACM 2nd International Conference on Security of 
Information and Networks (SIN 2009), North Cyprus 

[8] Abbas Haider, Raza Asad , Louise Yngstrom, Ahmed Hemani, "Evaluation of 
ESAM using Architectural Tradeoff Analysis Method", Project Report -VERVA, 
December 2008 

[9] Kurt Helenelund, Stephan Urdell, Bo Sehlberg, Anders Bremsjo, Anders 
Lindgren, Jan Lundh, Christer Marklund, "SHS Version 1.2 Protocols", VERVA - 
Swedish Administrative Development Agency, 2007 

[10] Wilson, D. ; Greig, A. ; Gilby, J. ; Smith, R., "Intelligent automated 
inspection, representing the uncertainty of the real world", IEE Colloquium on 
Intelligent Sensors (Digest No: 1996/261), 19 Sep 1996, pages 11/1 - 11/6 



[11] McVicker, M.; Avellino, P.; Rowe, N.C., "Automated Retrieval of Security 
Statistics from the World Wide Web" IEEE SMC Information Assurance and 
Security Workshop, 2007, 20-22 June 2007, pages 355 - 356 

[12] Abbas Haider, Yngstrom Louise and Hemani Ahmed, "Security Evaluation of 
IT Products: Bridging the Gap between Common Criteria (CC) and Real Option 
Thinking" in proceedings of World Congress on Engineering and Computer 
Science (WCECS 2008), 22-24 October, 2008, San Francisco, USA 

[13] Nick Jennings, Michael Wooldridge, "Software Agents", IEE Review, 
January 1996, pp 17-20 

[14] Thomas R. Gruber: Automatically Integrating Heterogeneous Ontologies 
from Structured Web Pages. Int. J. Semantic Web Inf. Syst. 3(1): 1-11 (2007) 

[15] Xiaomeng Su, Mihhail Matskin and Jinghai Rao. "Implementing 
Explanation Ontology for Agent System". In Proceedings of the 2003 
IEEE/WIC International Conference on Web Intelligence, WI'2003, Halifax, 
Canada, October, 2003. IEEE Computer Society Press 



AUTHORS PROFILE 

Haider Abbas has been working as doctoral student at 
Department of Electronic Systems, KTH, Sweden. Mr. Abbas 
has authored more than 10 international publications and has 



been working for various governmental and private projects in 
Pakistan and Sweden. 

Christer Magnusson is Senior Lecture at the Department of 
Computer and Systems Sciences, Stockholm University, 
specialized in IS/IT Security and IS/IT Risk Management. 
Before joining SecLab, Christer was Head of Corporate 
Security and Risk Manager at Sweden Post and CEO of 
Sweden Post Insurance AB and Sweden Post Reinsurance 
S.A. He has also held the position as Head of Corporate 
Security in the Ericsson group. In 1999, Christer was awarded 
the SIG Security Award by the Swedish Computer Society 
and in 2000 the Security Award by the Confederation of 
Swedish Enterprise as recognition of the models and the 
integrated processes regarding IS/IT Risk Management, that 
he developed as a part of his research studies. Dr. Christer is a 
member of the advisory committee of the Swedish Emergency 
Management Agency. He serves on the risk and security board 
of the Confederation of Swedish Enterprise (NSD). He is also 
an adviser in Corporate Governance, Compliance, Risk 
Management, and Information and ICT Security to 
government agencies as well as trade and industry. 

Louise Yngstrom is a professor in Computer and Systems 
Sciences with specialization in Security Informatics in the 
department of Computer and Systems Sciences at Stockholm 
University. Her research base is Systems Science which she 
since 1985 has applied within the area of ICT security forming 
holistic approaches. Her research focuses various aspects on 
how ICT security can be understood and thus managed by 
people in organizations, but also generally on criteria for 
control. She has been engaged in various activities of the 
International Federation of Information Processing, IFIP, since 
1973; the Technical Committee 3(TC3) with an educational 
scope, the TC9 with focus on social accountabilities of ICT 
structures and the TC11 with focus on ICT security. She 
founded the biannual conference WISE (World Conference on 
Security Education) in 1999. She was engaged in European 
networking for curricula developments within ICT security 
and the Secured Electronic Information in Society working for 
e-Identities during the 1990's. Since 2000 Dr. Louise is 
involved in introducing ICT security in academic and business 
life in African countries through her research students who 
simultaneously with their research are academic teachers in 
their home countries. Over the years she has traveled and 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 

networked extensively with international peers. Presently she Sweden. Dr. Hemani has authored more than 100 international 

is the principal advisor of seven PhD students. publications. He is participating in and leading some national 

and EU projects. 
Ahmed Hemani has been working as professor and head of 

post-graduate studies at Dept. of ES, School of ICT, KTH, 



67 http://sites.google.com/site/ijcsis/ 

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Driving Architectural Design through Bussiness Goals 



Lena Khaled 

Software Engineering Department 

Zarqa Private University 

Amman, Jordon 

lenaumleen@yahoo.com 



Abstract - Architectural design must encompass changes 
to business goals, their relations to quality attributes 
overtime and their results upon building the final 
specific systems. This paper discusses the effect of 
business goals on building the architectural design on 
any system. It describes the relationship between 
business goals and system qualities, and how these 
qualities are met during architectural design. This paper 
also describes how the qualities have an effect on the 
decisions of building the architectural design on any 
system. The role of the agent is described through the 
process of building. 

Keywords- Bussiness Goals; Qualities; Architectural Design 
Decisions. 



I. 



Introduction 



An important part in the design phase and construction of 
any complex system is its architecture. A good architecture is 
that system which satisfies key requirements such as 
performance, reliability, portability, interobelability, 
availability, security, safety, scalability and other attributes. 
A bad architecture causes a lot of damage. According to 
IEEE, "Architectural design is the fundamental part in any 
system which includes components of a system, their 
relationships to each other and to the environment and the 
basic principles which guide the design and evolution of that 
system" [5, 7] 

On the other hand, business goals are the foundation on 
which any architectural design is built upon. Architectural 
design is built to achieve mission goals. Business goals come 
in many types and at many levels of abstraction; therefore, 
architectural design is a bridge between business goals and 
the achieved system. 

This paper describes how business goals cause the 
progress to build architectural design to any system; many 
processes need to be applied in order to reach the final 
system. This paper is organized as the following: section 3 
defines business goals and attributes and then it defines the 
relation between them. Section 4 describes the architectural 
design decisions and its major phases when defining the 
design process. Section 5 defines the main result of this 
paper; it talks about how the final architecture is built, the 
role of software agent through building and software quality 
metrics. You can find the conclusion in section 6. 



II. Related Works 

The first piece of related works is reported in the most 
depth in Perry. Perry Dewayne and Wolf Alexander worked 
on studying the foundations of software architecture, they 
developed an intuition for software architecture by applying 
to several disciplines of architecture, and they presented a 
model of software architecture which consists of three 
components: elements, form and rationale [12]. 

The second related works is [5]. Garlan examined 
important trends of software architecture in research and 
practice, and speculated on the important emerging trends: 
aspiration and challenges. 

The third paper in related works is [1]. Ozkaya, Kazman 
and Klein worked on a case that represented the architectural 
patterns which carries the economic value in the form of real 
options. They summarized their observations in evaluating 
the relative value of patterns using real option value models 
on a model problem and they looked carefully on how 
economics-informed approaches can provide better insights 
for the selection of situated design strategies. 

The fourth paper in related works is [1]. This paper 
describes how architectural decisions are made from quality 
attributes. It presents a set of steps that allowed moving from 
quality attribute requirement to design fragment based on 
achieving that requirement. All of these are demonstrated 
through an application of an embedded system as in 
example. 

This paper works on the aim of business goals and their 
relationships to the quality attributes, and how this relation 
can drive the architectural design to any complex system. 

III. BUSINESS GOALS AND QUALITIES 

Understanding bussiness goals and their relations to the 
qualities is a critical part of building the architectural design 
of any system; we cannot easily use architectural design or 
other solutions without understanding the concepts of both 
bussiness goals and qualities. Therefore, these goals and 
qualities drive the architectural design of the system [3]. 

Driving architectural design through goals need an early 
method used to generate and refine qualities, which is called 
QAW (Quality Attribute Workshop). This gets qualities that 
are mapped to business goals scenarios for the qualities 
which are built by stakeholders according to the main goals. 
All these scenarios specify whether a system satisfies the 



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user's requirements or not [3,2]. The quality attributes must 
be well understood and expressed early in the development 
of the life cycle of the system, so the architect can design an 
architecture that will satisfy these qualities. QAW is a 
method to discover, document, prioritized the system's 
quality attributes early in its life cycle [2]. 

A. Bussiness goals 

Business goals are the parts that drive the methods of 
design, and are the elements that shape the architecture. The 
important thing is that all business goals that correspond to 
quality attributes will view the end of the system [3,6]. 

According to [10], we can define a goal as a state of 
events in the world that users would like to achieve. Goals 
can be rather business goals or system goals. Business goals 
are states that an individual or an organization wants to 
reach. 

B. Quality attributes 

In manufacturing, the concept of the quality means that 
the product should meet its requirements, but the popular 
vision of the quality is that it is an intangible attribute. 
Terms of bad or good quality represent how people talk 
about something vague which they don't propose to define. 
Quality attributes describe the property of the system that 
refers to its fitness for use. The term, non-functional 
requirement, is a synonym for quality attribute or attribute. 
[13,9,4] 

The international standard on software product qualities 
classifies software quality as six main attributes: 
functionality, reliability, usability, efficiency, 

maintainability, and portability. Despite the fact that there 
are many quality attributes, reliability and maintainability 
are the main quality criterions and many of these attributes 
are created at business levels and are better viewed as 
business goals [6, 8]. Figure 1 illustrates how goals and 
attributes affect each other. 



Business goals 


Affected by 


Quality 
Attributes 

















IV. ARCHITECTURAL DESIGN DESCISIONS 

Architectural design decisions play an important 
position in designing the architecture for any system. We can 
define an architectural design decision as: a description of 
the set of architectural modifications to the software, the 
principles and rules to the design, design constraints and 
additional requirements that realize requirements on a given 
architecture. During the process of architectural design, the 
software designer has to make a number of decisions that 
affect the system and its development process [13]. 

Figure 1 shows how the decisions of a designer affect 
the design process. According to the experience of the 
designer, the main decisions are the answer to the following 
questions [13]: What is the main approach that has been used 
to structure the system? What is the strategy that has been 
used to control the operations in the system? How is 
distribution across the occurring system? What is the 
appropriate style for the system? How do the evaluations of 
the architectural design occur? How should the architecture 
of the system be documented? 



Specification 



Designers Decision 



Design process 



Implementation 



Figure 2. The role of designer's decisions on a design process 



When developing systems, the process of the design is 
divided into two distinct phases. Figure 3 describes these 
phases [14]. 



Figure 1 . The relation between goals and attributes 



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Figure 3. The major phases of the software design process 

1) In the first phase, the designer develops a high level 
of solutions (the architectural or logical design) in which 
only the abstraction view of the model is described without 
details. Here, the system is described as black-box system. 

2) In the second phase, the abstract view is going into 
details(the details or physical design) . Turning the black 
box into white box , the output from this phase provides the 
specifications for the programmer. 

Making a decision to any architecture is based on the 
relation between goals and qualities. 

V. FINALIZING ARCHITECTURAL DESIGN 

The architectural design of any system can be defined as 
the structure of the system, which consists of software 
components, the external properties of these component and 
their relationships. The architectural design affects the 
performance, maintainability, robustness. The particular style 
and structure chosen for an application may therefore depend 
on non functional requirements [14, 13]. 

A. The role of the agent 

Software agents are autonomous software entities that 
navigate through environments and can either work alone or 
with other agents to achieve the goal. 

The software agent plays an important role in the cycle 
that is represented in figure 3. The fundamental reason for 
using an agent to build the architectural design for any 



system is based on the concept that many users 
(stakeholders) within an organization have many different 
business goals. Therefore, by linking software agent to this 
cycle, it becomes possible to give the main impact for 
choosing a specific architectural design decisions that arise 
through building the architecture [6]. The architect needs 
many decisions to build the final system. Then, the software 
agent helps the architect to choose the main decisions that 
make the architect achieving his goal to meet the user 
requirements. 



Architect 



Needs 




Figure 4. Driving Achitectural Design through bussiness goals 

Figure 4 represents the result of this paper; it describes 
how business goals are affected by the attributes and how 
this relation affects on the decision of the architect and in 
which from these decisions the final architecture is built. The 
role of the agent is described through the figure. The agent is 
especially raised when we want to move from old to new 
goals. 

Creating goals is an important thing to the products and 
process. These goals should be considered by the architect 
through mechanisms, which are driven by models of 
business. There are many types of mechanisms such as the 
SQM that appears in figure 4 and is defined as a mechanism 
for measuring goals. Measurements enable the organization 
to make its process better than before, in addition to the 
controlling of the software project and assessing the software 
quality. Other measurement can be used instead of SQM 
such as the Quality Function Deployment approach (QFD), 
the Goal/Question/Metric paradigm (GQM). 

Management is done better by measurement, 
measurement enables the organization to improve the 



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activities of the quality management: planning, control and 
asses the quality of the software that produced. 

The quality management activities check the project 
output to ensure that they are dependable with the 
organizational standards and goals. The development team 
should be independent from the quality assurance team so 
that they can take a goal view of the software. An 
independent team should be responsible for managing the 
quality to maintain the project schedule and budget. As a 
result, this team should not be associated with any particular 
development group. An independent team ensures that the 
organizational goals of quality are not compromised by 
schedule consideration and short-term budget [13]. 

The evaluation of the overall work will reflect its 
relationship to the quality of work that is expected from 
decisions which were made by the architect before. It is 
defined as the process of verifying satisfaction of 
requirements. The final architectural design is very difficult 
to be evaluated because the correct test of the architecture is 
in how it meets the functional and non-functional 
requirements after it has been deployed [13]. 



VI. CONCLUSION 

Architectural design is one of the most important phases 
in the life cycle of any system. Building an architectural 
design for any system to any organization must include the 
changes that occurred to business goals. This paper presents 
how an architectural design was drove from business goals 
and this is done through explaining the relation between 
business goals and non-functional requirements (also called 
quality attributes or attributes). It describes how an architect 
needs decisions to build the architecture and how these 
decisions are built upon the previous relation. The role of an 
agent is shown through this paper to show the movement 
from old business goals to new business goals. 



REFERENCES 

[I] Bachman F,Bass l 5 klein M, "Moving From Quality Attribute 
Requirmentto Architectural Decisions," Software 
Engineering Institute,USA. 

[2] Barbacci M,Ellison R,Lattanze A,Stafford J,Weinstock C,and 

Wood W,"Quality Attribute Workshops(QAW),technical 

report CMU/SEI,2003 . 
[3] Bass L,Clements P, Kazman R,Nord R, "Architectural 

Business Cycle Revisted," Software Engineering 

Institude,Caregie Mellon,2009. 
[4] Berenbach B,Paulish D,Kazmeier J,Rudorfer A, "Software 

and systems requirments engineeering in 

practice,"McGrawHll,2009. 
[5] David Garland,"Software Architecture: a Roadmap," ACM 

Press,2000. 
[6] Gross D, Yu E,"Evolving System Architecture to Meet 

Changing Bussiness Goals: an Agent and Goal -Oriented 

Aapproach," University of Toronto. 

[7] http://www.sei.cmu.edu/ Carnegie Mellon,2007. 
[8] Jalote P,"A concise introduction to software engineering," 
Spnnger,2008. 

[9] Kan S, "Metrics and models in software quality 
engineering," Addison Wesely,2003 . 

[10] Liu l,yu eric, "From Requirment to Architectural Design - 
using Goals and Scenarios," University of Toronto. 

[II] Ozkaya I, Kazman R, Klein M,"Quality attribute based 
economic valuation of architectural patters," SEI,9TH 
international conference on software engineering,IEEE,2007. 

[12] Perry D., Wolfs A., "Foundations of the study of software 
architecture," ACM Sigsoft, vlo.17, no. 4, 1992. 

[13] Sommerville I,"Software Engineering, "Pearson education, 
2007. 

[14] Widhani A,Boge S,Bartelt A,LamersdofW," Software 
architectural and patterns for electronic commerce 
systems, "University of Hamburg,2002. 



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DISTRIBUTED INFORMATION SHARING COOPERATION IN 
DYNAMIC CHANNEL ALLOCATION SCHEME 



Mr.PJesu Jayarin, 
Research Scholar, 
Sathyabama University, 
Chennai-119, India. 
jjayarin@gmail.com , 



Dr.T.Ravi, 

Prof&Head ,Dept of CSE, 
KCG college of Technology 
Chennai-97, India. 



Abstract 

The Channel allocation method is an 
Cooperative Asynchronous multichannel MAC 
which introduces an Distributed Information 
Sharing (DISH) to be used in a distributed flavor 
of control-plane cooperation, as a new approach 
to wireless protocol design and then apply it to 
multichannel medium access control(MAC) to 
solve the MCC problem. The basic idea is to 
allow nodes to share control information with 
each other such that nodes can make more 
informed decisions in communication. Medium 
access control (MAC) protocols play a major 
role to create a wireless communication 
infrastructure. In Wireless network, transmitter- 
receiver pairs make independent decisions, 
which are often suboptimal due to insufficient 
knowledge about the communication 
environment. So, the new concept DISH is 
introduced and overcomes the problem occurred 
in the MAC protocol. The DISH concept avoids 
collision and re-transmission among nodes. The 
notion of control-plane cooperation augments 
the conventional understanding of cooperation, 
which sits at the data plane as a data relaying 
mechanism. In a multichannel network, DISH 
allows neighboring nodes to notify transmitter- 
receiver pairs of channel conflicts and deaf 
terminals to prevent collisions and 
retransmissions. Based on this, we design a 
single-radio cooperative asynchronous 

multichannel MAC protocol called CAM-MAC 
When the CAM-MAC is used in illustration 
purposes, we choose a specific set of parameters 
for CAM-MAC. First we analyze the throughput 
to 91% of the system bandwidth to 96%, then 
saturate 15 channels and compare the result, this 
provides an good result in implementing 
hardware. 

Keywords-Distributed information sharing 
(DISH), control-plane cooperation, CAM-MAC, 
multichannel coordination problem, MAC 
protocol, ad hoc networks. 



1. INTRODUCTION 

The Cooperative asynchronous 
multichannel MAC is to allow nodes to share 
control information with each other such that 
nodes can make more informed decisions in 
communication. This notion of control-plane 
cooperation augments the conventional 
understanding of cooperation, which sits at the 
data plane as a mechanism for intermediate 
nodes to help relay data for source-destination 
pairs Asynchronous Multichannel MAC is to 
allow nodes to share control information with 
each other such that nodes can make more 
informed decisions in communication. 

The new approach DISH is 
implemented first and then medium access 
control to solve MCC problem. In a multichannel 
network, DISH allows neighboring nodes to 
notify transmitter-receiver pairs of channel 
conflicts and deaf terminals to prevent collisions 
and retransmissions. Based on this, we design a 
single-radio cooperative asynchronous 

multichannel MAC protocol called CAM-MAC. 
This is used to provide a good result in 
implementation. The heartily focused on giving a 
distributed solution in minimal cost, efficient and 
sufficient solution in the real world application is 
to allow the nodes to share the control 
information with all the other nodes in 
communication. Example for instance, transfers 
of data from one node to another node in a 
distributed environment without collision and re- 
transfer of data. DISH enables nodes to store 
channel usage information at their neighbors, and 
retrieve this information whenever it is needed. It 
is not a compulsory coordination mechanism but 
it provides an cooperation with other nodes and 
operates well at all condition. In this network 
does not rely on coordination and performs well. 
Most wireless LANs are single channel systems. 

However, as the number of nodes 
communicating increases, systems with a single 
channel suffer declining performance. 
Contributing to the problem are the well-known 



1 

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hidden and exposed terminal problems. To 
combat these problems there is growing interest 
in multi-channel systems. Indeed, the IEEE 
802.11 standard already has multiple channels 
available for use. The IEEE 802.11a physical 
layer has 12 channels, 8 for indoor and 4 for 
outdoor use. IEEE 802.11b has 14 channels, 
5MHz a part in frequency. To avoid channel 
overlap, the channels should have at least 30MHz 
guard bands; typically, channels 1, 6 and 11 are 
used for communication. In a multi-channel 
system, the transmitter and receiver must both 
use an agreed upon channel for communication. 
This introduces a channel coordination problem. 
As well, the hidden and exposed terminal 
problems remain in the multichannel setting. 



c 



D 






*■••*; j>* 



E 



- neighbor relationship 
••► new communication 



C 
® 



(a) 
D 
® 



ongoing data transmission 
cooperation 



E 
® 



1 X 




10K 




1 X 


2 OK 




2 OK 




2 OK 


3 OK 




3 X 




3 X 


L_/~^ 




Ly^ 




Ly^ 



(b) 

Fig. 1. An illustration of the DISH idea, (a) A 
multichannel scenario. 

(b) Knowledge at individual nodes. By consolidating 
the knowledge at nodes C and D, or acquiring 
knowledge from node E, it shows that the conflict-free 
channel is channel 2. 

Based on the idea of DISH, we design a 
single-radio cooperative asynchronous 

multichannel MAC protocol called CAM-MAC 
for ad hoc networks. We evaluate CAM-MAC 
from both theoretical and practical perspectives, 
where we choose a specific set of protocol 
parameters for illustration and evaluation 
purposes: 

1 .We show that its throughput upper bound 
is 91 percent of the system bandwidth and its 
average throughput approaches this upper 
bound with a mere gap of 4 percent, 
2.We show that it can saturate 15 
channels at maximum and 14.2 channels on 



average, which indicates that, although CAM- 
MAC uses a control channel, it does not 
realistically suffer from control channel 
bottleneck, 

3. To investigate the value of cooperation we 
compare MAC and observe a throughput ratio of 
2.81 and 1.70 between them in single-hop and 
multihop networks, respectively, 
4. We compare CAM-MAC with three recent and 
representative multichannel MAC protocols, 
MMAC, SSCH, and AMCP , and the results 
show that CAM-MAC substantially outperforms 
all of them. 

For a further and more realistic 
validation, we implemented CAM-MAC on 
COTS hardware and conducted experiments. To 
the best of our knowledge, these prototypes are 
the first full implementation of single-radio 
asynchronous multichannel MAC protocols. 

We review literature in Section 2, and 
identify new challenges to designing a 
cooperative protocol in Section 3. Then, we 
present the protocol details in Section 4. 
Following that, Section 5 provides performance 
results in various scenarios, and Section 6 
describes our implementation and experiments, 
and, finally, give concluding remarks in Section 
7. 

2. MULTICHANNEL MAC 
PROTOCOLS 

Multichannel MAC protocols for ad hoc 
networks can be categorized into two general 
classes as below. 

2.1 Single-Radio Solutions 

2.1.1 Control-Data Window Schemes 

MMAC assumes the IEEE 802.11 
power saving mode and divides time into beacon 
intervals. Each beacon interval is 100 ms and 
consists of a 20-ms ATIM window and an 80-ms 
data window. Nodes negotiate and reserve 
channels in the ATIM window on a common 
channel, and transmit data in the data window on 
multiple channels concurrently. The data 
window size is fixed, and hence, it has to be set 
according to the maximum data packet size, 
leading to inefficiency. By contrast, MAP varies 
the data window size and avoids this problem. 
However, like MMAC, its reservation interval 
(i.e., control window) is still fixed, and thus, 
both protocols suffer from the inflexibility to 
different node densities: at low density, the 
control window has long idle time; at high 
density, the control window cannot 



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accommodate all negotiations and some nodes 
have to wait for the next control window. 
Further-more, MM AC and MAP requires time 
synchronization over the entire network, which is 
a notoriously hard task involving considerable 
overhead and complexity, and compromises 
scalability. LCM MAC, on the other hand, 
allows each neighborhood to negotiate the 
boundaries of control-data windows 
independently, in order to avoid time 
synchronization. However, the negotiated 
window size can hardly fit for all nodes in the 
neighbor-hood, and this window negotiation, 
plus a fine-tuning mechanism, considerably 
complicates the protocol. Besides, it has a 
starvation problem lacking in an appropriate 
solution. Finally, all these control-data window 
schemes have a common problem: during each 
control window, all channels other than the 
common channel cannot be utilized, resulting in 
channel underutilization. 

2. 1 .2 Channel Hopping Schemes 

In SSCH , each node hops among all 
channels according to a pseudorandom sequence 
such that neighboring nodes will have channels 
overlap in time periodically. Since a trans-mitter 
can only communicate to a receiver when they 
hop. In CAM-MAC, each node stays on a 
common channel and only switches channel 
when a data exchange is established successfully 
or finished. This avoids switching channel too 
often and, due to the common channel, does not 
incur large delay. Besides, again, no clock 
synchronization is required. 

2.1.3 Routing and Channel Assignment 
Schemes 

CBCA combines channel assignment 
with routing. It proposes to assign each set of 
intersected flows, called a component, with a 
single channel, in order to avoid channel 
switching delay, node synchronization, and 
scheduling overhead at flow-intersecting nodes. 
CAM-MAC uses a control channel, which 
automatically avoids the problem of node 
synchronization and scheduling overhead. 
Regarding channel switching delay, its effect on 
network performance is much less than MCC 
problems: the channel switching delay is 40-150 
(is but a channel conflict can collide at least one 
data packet whose delivery several and even tens 
of milliseconds. 

In fact, CBCA shifts complexity from 
the MAC layer to the routing layer. Also, 



compared to packet, link, and flow-based 
channel assignments, it has the least flexibility in 
exploiting multichannel diversity. Each 
component, which spans all intersecting flows, 
can only use one channel. As a consequence, any 
two nodes in a common component cannot 
transmit simultaneously unless they are at least 
three or four hops apart (depending on the 
interference range). In a single-hop network, 
since all flows are intersected, a multichannel 
network degrades to a single-channel network. 

2.2 Multiradio Solutions 

Using multiple radios can easily solve 
MCC problems by dedicating one radio for 
monitoring channel usage information.DCA uses 
two transceivers, one for exchanging control 
packets and the other for exchanging data 
packets. The control packets are used to allocate 
the channels on the data transceiver on demand. 
A multichannel CSMA protocol assumes the 
number of channels to be equal to the number of 
transceivers per node, so that all channels can be 
used simultaneously. This is very expensive. A 
protocol similar to DCA in that it also dedicates 
a transceiver for control purposes, but the 
difference is that channel selection is done at the 
receiver end based on signal-to-noise ratio. MUP 
also uses two transceivers but it allows both 
transceivers to exchange control messages and 
data packets. xRDT extends RDT, which uses a 
(possibly different) quiescent channel for each 
node to receive packets, by adding a busy-tone 
radio to each node in order to inform the 
neighborhood of ongoing data reception, in order 
to avoid collision and deafness. It proposed link- 
layer protocols for routing in multiradio 
multichannel ad hoc networks. Each node is 
assigned a fixed interface for receiving packets 
and multiple switchable interfaces for 
transmitting packets. This is similar to the idea 
of quiescent channel but uses more radios to 
simplify overcoming MCC problems. Obviously, 
the key drawback of multiradio protocols is the 
increase of device size, cost, and potentially 
energy consumption. 

3. CAVEATS TO COOPERATIVE 
PROTOCOL DESIGN 

We identify three major issues in 
designing a cooperative MAC protocol, which 
will adversely affect protocol performance unless 
properly addressed. 

3.1 Control Channel Bottleneck 



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Using a dedicated control channel can 
facilitate the design of a cooperative protocol, 
because a control channel provides a unique 
rendezvous for nodes to disseminate, gather, and 
share information. However, this design scheme 
may come with a drawback: When a large 
number of channels and nodes are present, the 
single control channel which is used to set up 
communications can be highly congested and 
become a performance bottleneck. 

3.2 Cooperation Coordination 

An MCC problem can be identified by 
multiple neighboring nodes, and hence, their 
simultaneous response of sending cooperative 
messages will result in collision. This creates an 
issue of cooperation coordination. One solution 
is to make neighbors sequentially respond via a 
priority-based or slot-based mechanism, thereby 
ensuring all cooperative messages to be 
transmitted without collision. However, this is 
very inefficient because 1) there can be many 
wasted (idle) intervals because not all neighbors 
may identify the problem and 2) cooperative 
messages pertaining to the same MCC problem 
carry redundant information, and hence, 
receiving all of them is not necessary. Another 
solution is to let each neighbor send such 
messages probabilistically, in order to reduce the 
chance of collision. However, an optimal 
probability (optimal in the sense of minimizing 
the chance of collision) is hard to determine, and 
such a scheme can result in no response which 
essentially removes cooperation. Therefore, a 
simple yet effective coordination mechanism is 
needed. 




((«» 



cooperation p, w 



U2 [J interference 



h 



Fig. 2. Cooperation interference 

3.3 Cooperation Interference 

This issue means that the cooperative 



messages sent by neighbors for a transmit- 
receive pair can unconsciously cause interference 
to another (nearby) transmit-receive pair. This is 
a new type of interference created by the 
introduction of cooperation, and our simulations 
found that it frequently happens and consider- 
ably intensifies channel contention. As such, a 
mechanism needs to be devised to address this 
deleterious side effect. 

4. PROTOCOL DESIGN AND 
ANALYSIS 

Our assumption is that each node is 
equipped with a single half-duplex transceiver 
that can dynamically switch between a set of 
orthogonal frequency channels but can only use 
one at a time. We do not assume specific channel 
selection strategies; CAM-MAC runs on top of 
any such strategy. For quantitative performance 
evaluation, we will consider two strategies in 
simulations and experiments: 1) RAND 
selection, where a node randomly selects one 
from a list of channels that it deems free based 
on its knowledge, and 2) most recently used 
(MRU) selection, where a node always selects its 
MRU data channel unless it finds the channel to 
be occupied by other nodes, in which case 
RAND selection strategy is used. 

We do not assume equal channel 
bandwidth or channel conditions such as noise 
levels; these can be taken into account by 
channel selection strategies (e.g., choose the 
channel with the highest SNR) which are not in 
our assumptions. We also do not assume any 
(regular) radio propagation patterns, nor assume 
any relationship between communication ranges 
and interference ranges.Intuitively,none of the 
nodes is responsible for providing cooperation; a 
node cooperates if it can (it is idle and overhears 
a handshake that creates an MCC problem), and 
simply does not cooperate . Actually, there often 
exists at least one neighboring node that can 
cooperate, and even in the worse where no one 
can cooperate, the protocol still proceeds (as a 
traditional noncooperative protocol). 

4.1 Protocol Design 

One channel is designated as the control 
channel and the other channels are designated as 
data channels. A transmitter and a receiver 
perform a handshake on the control channel to 
set up communication and then switch to their 
chosen data channel to perform a DATA/ACK 



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handshake, after which they switch back to the 
control channel. A transmitter sends a PR A and 
its receiver responds with a PRB, like IEEE 
802.11 RTS/CTS for channel reservation. 
Meanwhile, this PRA/PRB also probes the 
neighborhood inquiring whether an MCC 
problem is created (in the case of a deaf terminal 
problem, it is probed by PR A only). Upon the 
reception of the PRA or PRB, each neighbor 
performs a check and, if identifying an MCC 
problem, sends an INV message to invalidate the 
handshake (the receiver can also send INV after 
receiving PRA, since it is also one of the 
transmitter's neighbors). If no INV is sent and 
the transmitter correctly receives PRB, it sends a 
CFA to confirm the validity of PRA to all its 
neighbors (including the receiver), and the 
receiver will send a CFB to confirm the validity 
of the PRB if it correctly receives CFA. This 
marks the end of a control channel handshake. If 
any INV is sent, the handshake will not proceed 
and the transmitter will back off. The NCF is 
merely used by the transmitter to inform its 
neighbors that the PRA and CFA are invalid 
when it fails to receive CFB (the receiver gets 
INV after sending PRB). 

cooperative collision avoidance period 

SIFS 
CCA 



Transmitter. 



PRA 



CFA 



CFB TMOUT 



NCF 



Transmitter's 
neighbor 



Receiver - 

Receiver's 
neighbor " 



CFB 



PRA/PRB: probe If 
CFA/CFB: confirmation NCF: negative 



others in its vicinity cancel sending their INVs (a 
receiver can also cancel its PRB). 

_ 6 6 11 

PRA/PRB F 



INV 



TA 


RA 


|CH|seq | 


6 


6 


1 2 


TA 


RA 


| CH | duration | 



CFA/CFB seq duration 



NCF/ACK seq 



TA: transmitter address 
RA: receiver address 
CH: channel index 



Fig. 4. A possible set of frame formats 

Channel usage table, shown in Fig. 5. 
Note that the until column does not imply clock 
synchronization: It is calculated by adding the 
duration in a received CFA/ CFB/INV message 
to the node's own clock. Similarly, when 
sending INV, a node does a reverse conversion 
from until to duration using a subtraction. 



TA 


RA 


CH 


until 


-4i 


A 2 


l 


11:30:52 


Bi 


Bo 


3 


11:30:56 



Fig. 5. Channel usage table. 

Also note that this table is by caching overheard 
information while not by sensing data channels. 
This is because sensing data channels often 
obtains different channel status at the transmitter 
and the receiver, and resolving this discrepancy 
adds protocol complexity. In addition, this may 
lead to more channel switching's and radio mode 
(TX/RX/IDLE) changes and thus incurs longer 
delay. 

4.2 Caveats Revisited 

Now, we explain how we address the 
caveats stated in Section 3 in the design of 
CAM-MAC. 



Fig. 3. The CAM-MAC control channel handshake. 

The cooperative collision avoidance 
period is for mitigating INV collision caused by 
multiple neighbors sending INVs 

simultaneously. It is a simple CSMA-based 
mechanism where each neighbor schedules to 
send INV at a random point in this period and 
continues sensing the channel. Once the node 
that schedules at the earliest time starts to send, 



4.2. 1 Cooperation Coordination 

Recall that this issue is to coordinate 
multiple neighbors to send cooperative messages 
as efficiently as possible. We address this using 
the cooperative collision avoidance period 
described in Section 4.1. It ensures that only one 
node will send out a cooperative message (INV) 
in each single-broad-cast region, assuming that 
propagation delay is negligible. We found via 
simulations that this can reduce 70 percent-85 



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percent collisions between cooperative messages. 
In case that collisions still happen (due 
to propagation delay or because not all 
cooperative nodes can hear each other), it is not a 
serious problem because CAM-MAC makes 
such collisions meaningful by using negative 
feedback only. That is, since INV always means 
invalidation, a collision resulting from INV still 
conveys that the hand-shake should not proceed. 
Actually, using negative feed-back is a logical 
design. First, a node expects a binary feedback 
since it selects one channel, instead of selecting a 
list of channels which needs multibit feedback 
indicating busy/free channels. Second, sending a 
positive feedback can be misleading because 
ensuring no MCC problem requires full 
information while a cooperative node cannot 
guarantee to have. 

5. PERFORMANCE EVALUATION 

We evaluate and compare five 
protocols, namely IEEE 802.11, CAMMAC- 
RAND, CAMMAC-MRU, UNCOOP-RAND, 
and UNCOOP-MRU, using a discrete-event 
simulator which we developed on Fedora Core 5 
with a Linux kernel of version 2.6.9. In these 
five protocols, IEEE 802.11 is used as a baseline 
in comparison, X-RAND and X-MRU are two 
versions of protocol X using RAND and MRU 
channel selection strategies, respectively. The 
protocol UNCOOP is identical to CAM-MAC 
except that the cooperation element is removed, 
i.e., neighboring nodes do not participate in 
communication by sending INV messages. This 
comparison will enable us to investigate the 
value of cooperation. 

We use three performance metrics: 1) 
aggregate (end-to-end) throughput, 2) data 
channel conflict rate, defined as the packet 
collisions on data channels per second over all 
nodes, and 3) packet delivery ratio, defined as 
the number of data packets successfully received 
by destinations. There is one control channel and 
five data channels with bandwidth 1 Mbps each. 
PHY and other MAC layer parameters, i.e., 
PLCP, SIFS, and retry limit, are the same as in 
IEEE 802.11 . Each source generates data 
packets with 2-Kbyte payload according to a 
Poisson point process. The cooperative collision 
avoidance period is 35jus . In the comparison of 
CAM-MAC and UNCOOP, we ignore channel 
switching delay as both protocols use the same 



handshake. However, in comparison to the other 
protocols, namely MMAC, SSCH, and AMCP, 
we use the parameters that they, respectively, 
use, including channel switching delay. The 
evaluation is based on 1, Impact of traffic load 2, 
Impact of data payload size 3, Impact of the 
number of nodes. 

6. IMPLEMENTATION 

There are 30 nodes forming 15 disjoint 
flows in a single-hop network. In the first set of 
simulations, the flows are always backlogged 
and the number of channels varies from 2 to 12. 
We see that CAM-MAC achieves a throughput 
of 11.86 Mbps while AMCP achieves 8.5 Mbps 
when there are 12 channels, which indicates a 
ratio of 1.40. Furthermore, AMCP saturates at 10 
channels whereas CAM-MAC still exhibits a 
growing trend beyond 12 channels. In the second 
set of simulations, there are four channels and 
the traffic generation rate varies from 8 Kbps to 
8 Mbps.Both CAM-MAC and AMCP have equal 
throughput at light traffic load, but apparent 
difference appears at medium to high load, and 
finally, CAM-MAC saturates at 5 Mbps while 
AMCP saturates at 4.2 Mbps. 



■?n 





__* ■— — ■— 










-*-SSCH 
-■-CAM-MAC 















5 10 

Number ai Flows 



i r - 



;a; 




L> 10 15 

Number c* Flgttfs 



o» 



Fig. 6. Comparison with SSCH. (a) Disjoint flows, (b) 
Nondisjoint flows 



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We implemented CAMMAC-RAND, CAMMAC- 
MRU, UNCOOP-RAND, and UNCOOP-MRU on 
COTS hardware implementation of single-radio 
asynchronous multichannel MAC protocols for ad 
hoc networks and a multichannel time 
synchronization protocol. It periodically exchanges 
beacon packets but data handshaking was not 
implemented. It provide a test bed for routing and 
channel assignment via statical manual 
configuration instead of hardware implementation. 
In a multichannel MAC protocol, which is designed 
for sensor network data collection applications 
assuming the many-to-one traffic pattern. 




w 




H>' 10 s 1D a 

Traffic Geneialinn Rate per Flaw (kb*l/a) 



\qT 



(b] 

Fig. 7. Comparison with AMCP. (a) Throughput 
versus number of channels, (b) Throughput versus 
traffic load. Four channels. 

7 .CONCLUSION 

Here, we have introduced DISH, which 
is a distributed flavor of control-plane 
cooperation, as a new approach to wireless 
protocol design. It enables transmitter-receiver 
pairs to exploit the knowledge at individual idle 
neighbors to make more informed decisions in 
communication. Applying DISH to multichannel 



ad hoc networks, we propose a cooperative 
multichannel MAC protocol called CAM-MAC, 
where idle neighbors share control information 
with transmitter-receiver pairs to overcome MCC 
problems. This protocol uses a single transceiver 
and, unlike many other protocols, is fully 
asynchronous. 

The simple idea of DISH turns out to be 
very effective. In the comparison of CAM-MAC 
with and without DISH, we observe remarkable 
performance difference. In the comparison with 
three recent and representative multichannel 
MAC protocols, MMAC, SSCH, and AMCP, 
CAM-MAC significantly outperforms all of 
them. Our implementation on COTS hardware 
and experiments further validates the advantages 
of CAM-MAC and the DISH idea. 

In a sense, DISH enables nodes to store 
channel usage information at their neighbors, and 
retrieve this information when it is needed. We 
also highlight that this is not a compulsory 
coordination mechanism, a network does not rely 
on cooperation and still operates when 
cooperation is not available. Ultimately, we 
believe that control-plane cooperation merits due 
consideration in the future design of wireless 
network protocols. 

8. ACKNOWLEDGEMENT 

We take immense pleasure in thanking 
our chairman Dr. Jeppiaar M.A, B.L, Ph.D, the 
Directors of Jeppiaar Engineering College 
Mr. Marie Wilson, B.Tech, MBA, (Ph.D), 
Mrs. Regeena Wilson, B.Tech, MBA, (Ph.D)and 
the principal Dr. Sushil Lai Das M.Sc(Engg.), 
Ph.D for their continual support and guidance. 
We would like to extend our thanks to my guide, 
our friends and family members without whose 
inspiration and support our efforts would not 
have come to true. Above all, we would like to 
thank God for making all our efforts success. 

9. REFERENCES 



[1] T. Luo, M. Motani, and V. Srinivasan, 
"CAM-MAC: A Coopera-tive 

Asynchronous Multi-Channel MAC 
Protocol for Ad Hoc Networks," Proc. IEEE 
Third Int'l Conf. Broadband Comm., 
Networks and Systems (BROADNETS '06), 
Oct. 2006. 

[2] J. So and N. Vaidya, "Multi-Channel MAC 



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for Ad Hoc Networks: Handling Multi- 
Channel Hidden Terminals Using a Single 
Transceiver," Proc. ACM MobiHoc, 2004. 

[3] P. Bahl, R. Chandra, and J. Dunagan, 
"SSCH: Slotted Seeded Channel Hopping 
for Capacity Improvement in IEEE 802.11 
Ad-Hoc Wireless Networks," Proc. ACM 
MobiCom, 2004. 

[4] J. Shi, T. Salonidis, and E.W. Knightly, 
"Starvation Mitigation through Multi- 
Channel Coordination in CSMA Multi-Hop 
Wire-less Networks," Proc. ACM MobiHoc, 
2006. 

[5] S.-L. Wu, C.-Y. Lin, Y.-C. Tseng, and J.-P. 
Sheu, "A New Multi-Channel MAC 
Protocol with On-Demand Channel 
Assignment for Multi-Hop Mobile Ad Hoc 
Networks," Proc. Int'l Symp. Parallel 
Architectures, Algorithms and Networks 
(ISPAN), 2000. 

[6] A. Nasipuri, J. Zhuang, and S.R. Das, "A 
Multichannel CSMA MAC Protocol for 
Multihop Wireless Networks," Proc. IEEE 
Wireless Comm. and Networking Conf. 
(WCNC), 1999. 

[7] A. Nasipuri and J. Mondhe, "Multichannel 
CSMA with Signal Power-Based Channel 
Selection for Multihop Wireless Networks," 
Proc. IEEE Vehicular Technology Conf. 
(VTC), 2000. 

[8] N. Jain, S.R. Das, and A. Nasipuri, "A 
Multichannel CSMA MAC Protocol with 
Receiver-Based Channel Selection for 
Multihop Wireless Networks," Proc. 10th 
Int'l Conf. Computer Comm. and Networks 
(ICCCN), 2001. 

[9] A. Adya, P. Bahl, J. Padhye, and A. 
Wolman, "A Multi-Radio Unification 
Protocol for IEEE 802.11 Wireless 
Networks," Proc. IEEE First Int'l Conf. 
Broadband Comm., Networks and Systems 
(BROADNETS), 2004. 




Dr. T. Ravi, B.E, M.E, Ph.D 
is a Professor & Head of the 
Department of CSE at 
KCG college of 

Technology, Chennai. He 
has more than 18 years of 
teaching experience in 
various engineering 

institutions .He has published more than 20 
papers in International Conferences & Journals. 



AUTHORS PROFILE 




P. Jesu Jayarin B.E., M.E., 
(Ph.D) working as an 
Assistant Professor at 
Jeppiaar Engineering 

College, Chennai and he 
has more than 5 years of 
teaching experience. His 
areas of specializations are 

Mobile Computing, Computer Networks, 

Network security and TCP/IP. 



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KEY GENERATION FOR AES USING BIO-METIC FINGER 
PRINT FOR NETWORK DATA SECURITY 



1. Dr.R.Seshadri 

Director 
University Computer Center 
Sri Venkateswara University, Tirupati 
ravalaseshadri @ gmail. com 



2. T.Raghu Trivedi 

Research Scholar, 
Department of Computer Science 
Sri Venkateswara University, Tirupati. 
tamirisajtl @y ahoo.com 



Abstract 

Encryption is one of the Essential security 
technologies for computer data, and it will go a 
long way toward securing information. The 
unauthorized thefts in our society have made 
the requirement for reliable information 
security mechanisms. Even though information 
security can be accomplished with the help of 
a prevailing tool like cryptography, protecting 
the confidentiality of the cryptographic keys is 
one of the significant issues to be deal with. 
Here we proposed a biometric-crypto system 
which generates a cryptographic key from the 
Finger prints for encrypting and decrypting the 
information the popular biometric used to 
authenticate a person is fingerprint which is 
unique and permanent through out a person's 
life. Hence, the biometric is gone eternally and 
possibly for all the applications. If your 
information traverses on net to reach 
destination a number of attacks may be done. 
To protect from attacks we proposed a system 
which will encrypt the data using AES with 
biometric based key generation technique. 

Key Words! Decryption, Encryption, 
Histogram Equalization, Minutiae points, 
Morphological Operation. 

1. Introduction 

Here we are using the AES for the 
encryption and Decryption process. For AES 
Key is important. Protecting the confidentiality 
of the cryptographic keys is one of the 
significant issues. We generate the key from 
the receiver's finger print template. The 
security to that is provided with the help of 
finger print of the sender. 



Biometric Cryptographic Key 
Generators, or BKGs, follow a similar design: 
during an enrollment phase, biometric samples 
from a user are collected; statistical functions, 
or features, are applied to the samples; and 
some representation of the output of these 
features is stored in a data structure called a 
biometric template. Later, the same user can 
present another sample, which is processed 
with the stored template to reproduce a key [1]. 

Initially, the fingerprints are 
employed to extract the minutiae points which 
are transformed in an efficient manner to 
obtain deformed points. Subsequently, the 
deformed points are employed to generate the 
cancelable templates which are utilized for the 
extraction of irrevocable keys. 

One way to protect privacy is to encrypt the 
information we used a system which will 
encrypt the data using AES with Novel method 
of biometrics based key generation technique. 
Biometric crypto systems can operate in one of 
the following three modes l.Key Release 
2. Key binding 3. Key generation. 

In the key Release mode Biometric 
authentication is decoupled from the key 
release mechanism. The biometric template 
and key are stored as separate entities and the 
key is released only if the biometric matching 
is successful 

.In the key binding mode the key and 
the templates are monolithically bound with in 
a cryptographic frame work. It is 
computationally infeasible to decode the key 
or template without any knowledge of the 
user's biometric data. A crypto biometric 
matching algorithm is used to perform 
authentication and key release in a single step. 



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In the key generation mode the key is 
derived directly from the biometric data and is 
not stored in the data base. 

Though it is easy to implement a 
biometric crypto system in the key release 
mode such a system is not appropriate for a 
high security application because the biometric 
template is not secure template security is 
critical issue because stolen templates cannot 
be revoked. Key binding mode are more 
secure but difficult to implement due to large 
intra class variations in biometric data i.e. 
samples of the same biometric trait of user 
obtained over a period of time differ 
substantially. 

2.AES 

Encryption is one of the securities 
Technique for the information/data traversing 
through network. Encryption is process of 
transforming information using an algorithm to 
make it unreadable (cipher) to any one except 
sender and receiver. An encryption algorithm 
along with a key used in encryption and 
decryption of data .AES is one of the popular 
Algorithms used in symmetric key 
cryptography [3, 6]. It is symmetric block 
cipher that can encrypt/decrypt information. It 
is used at top secret level. AES supports key 
sizes 128,192 and 256 bits and will serve as 
replacement for the DES, which has a less key 
size. AES can encrypt data much faster than 
DES enhancement (Triple DES).. 

• Cipher text=E K (plain text) 

• Plain text= D K (cipher text) 

• D K (E K (plain text))=plain text 

3. Cryptography and biometrics 

Cryptography provides security to the 
information which is transferring over the 
insecure channel. Here key will play a major 
role, because it is used for encryption as well 
as for decryption. Lengthy key used to encrypt 
and decrypt sending and receiving the 
messages respectively. These keys can be 
guessed/ cracked. Maintaing and sharing the 
lengthy keys is critical problem. This can be 
overcome with the help of biometric system 
[5]. 

There are different biometric techniques. We 
concentrate on fingerer prints to generate key. 



Key vector Is formed based on minutiae points 
(ridge ending and ridge bifurcation) 



Finger Print Image 



Minutiae feature extraction 



Minutiae point location 
Set 



Key Vector Generation 



Figure 1: Generating key vector from finger 
print 

4. Key Generation from Finger Print 

Here we have to extract the minutiae points 
from the fingerprint to generate the key. The 
process is as below 



Finger Print 




v 




Preprocessing 




v 




Binarized 




i 


r 




Morphological 
Operation 




i 


r 




Minutiae Extraction 




i 


r 




False Minutiae Removal 


i 


r 


Key Generation 



Figure2: Key generation from minutiae 
points 



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4.1 Extraction of Minutiae Points from 
Fingerprints 

The extraction of minutiae points 
from the fingerprint image is discussed in this 
section. It is supposed that fingerprints are 
distinct across individuals and across the 
fingers of a particular individual [1] . Since 
many existing fingerprint authentication 
systems are based on minutiae points, which 
are feature points extracted from a raw 
fingerprint image, we have employed the 
minutiae points in our scheme as well. 

A fingerprint can be defined as a pattern of 
ridges and valleys on the tip of the finger. A 
fingerprint is therefore described by the 
distinctiveness of the local ridge features and 
their relationships. Minutiae points denote 
these local ridge characteristics that appear 
either at a ridge ending or a ridge bifurcation. 
The point where the ridge comes to an abrupt 
end is known as ridge ending and the ridge 
bifurcation is denoted as the point where the 
ridge divides into two or more branches [1]. 
The major steps involved in the minutiae 
points' extraction are as follows: 

• Segmentation 

• Minutiae Extraction 

4.1.1Segmentation 

The first step in the minutiae points' extraction 
is segmentation. The input fingerprint image is 
segmented from the background to actually 
extract the region comprising the fingerprint, 
which ensures the removal of noise. 
Segmentation of an image represents the 
division or separation of the image into regions 
that have similar attributes. At first, the image 
is pre-processed. The pre-processing phase 
includes the following: histogram equalization. 
Later, the pre-processed image is divided into 
blocks and segmentation is carried out. The 
sample fingerprint images are shown 




Figure 3: Two Sample Fingerprint Images 
4.1.1.1 Pre-Processing 

The pre-processing of fingerprint images 
includes Histogram Equalization 

4.1.1.1. a. Histogram Fqualization 

Histogram equalization amplifies the local 
contrast of the images, particularly when they 
are represented with very close contrast values. 
It is possible to distribute intensity through the 
histogram with the aid of this regulation. 
Histogram equalization utilizes a mono tonic, 
non-linear mapping that re-assigns the 
intensity values of pixels in the input image in 
such a manner that the output image comprises 
a uniform distribution of intensities (i.e. a flat 
histogram). The original histogram of a 
Fingerprint image is of bimodal type, the 
histogram after the histogram equalization 
transforms all the range from to 255 which 
results if enhanced visualization effect [7]. The 
results of histogram equalization are depicted 
in Figure 4. 



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Figure 4: Fingerprint Images after 
Histogram Equalization 



binarization process. Then, the pixel value is 
set to a binary value one when the value is 
greater than the global threshold, or else a zero 
is set as the pixel value. The foreground ridges 
and the background valleys are the two level of 
information held by the ensuing binary image. 
Removal of distortions present in the image is 
performed followed by the retrieval of the 
exact skeleton image from the image. 

4.1.2.2 Morphological Operation 

The binary morphological operators are 
applied on the binarized fingerprint image. 
Elimination of any obstacles and noise from 
the image is the primary function of the 
morphological operators. Furthermore, the 
unnecessary spurs, bridges and line breaks are 
removed by these operators. Then thinning 
process is performed to reduce the thickness of 
the lines so that the lines are only represented 
except the other regions of the image. Clean 
operator, Hbreak operator, Spur operator and 
Thinning are the morphological operators 
applied. 



4.1.2 Minutiae Points Extraction 



4.1.2.3 Minutiae points' extraction 



Finally, the minutiae points are extracted from 
the enhanced fingerprint image. The steps 
involved in the extraction of minutiae points 
are as follows: 

• Binarization 

• Morphological Operations 

• Minutiae points' extraction 

Initially, the enhanced image is binarized. 
After binarization, morphological operations 
are performed on the image to remove the 
obstacles and noise from it. Finally, the 
minutiae points are extracted using the 
approach discussed. 

4.1.2.1 Binarization 

The binary images with only two levels of 
interest: The black pixels that denote ridges 
and the white pixels that denote valleys are 
employed by almost all minutiae extraction 
algorithms. A grey level image is translated 
into a binary image in the process of 
binarization, by which the contrast between the 
ridges and valleys in a fingerprint image is 
improved. Hence, the extraction of minutiae is 
achievable. The grey-level value of every pixel 
in the enhanced image is analyzed in the 



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Figure 5: 
Points 



Fingerprint Images with Minutiae 



Ridge Thinning is to eliminate the redundant 
pixels of ridges till the ridges are just one pixel 
wide, uses an iterative, parallel thinning 
algorithm. In each scan of the full fingerprint 
image, the algorithm marks down redundant 
pixels in each small image window (3x3). And 
finally removes all those marked pixels after 
several scans. After the fingerprint ridge 
thinning, marking minutia point is relatively 
easy .For each 3X3 window, if the central 
pixel is 1 and has exactly 3 one-value 
neighbors, then the central pixel is a ridge 
branch, if the central pixel is 1 and has only 1 
one -value neighbors , then the central pixel is 
ridge ending. Suppose both the uppermost 
pixel with value 1 and the rightmost pixel with 
value 1 have another neighbor outside the 3X3 
window, so the two pixels will be marked as 



branches too. But actually only one branch is 
located in the small region. So a check routine 
requiring that none of the neighbors of a 
branch are branches is added. 

4.1.2.4 False Minutia Removal: 

The preprocessing stage does not totally heal 
the fingerprint image .For example, false ridge 
breaks due to insufficient amount of ink and 
ridge cross-connections due to over inking are 
not totally eliminated. Actually all the earlier 
stages themselves occasionally introduce some 
artifacts which later lead to spurious minutia. 
These false minutias will significantly affect 
the accuracy of matching if they are simply 
regarded as genuine minutia. So some 
mechanisms of removing false minutia are 
essential to keep the fingerprint verification 
system effective. 

Key Generation From Minutiae Points 

In this section we explain the key Generation 
Algorithm [2] Assumptions 

Kl 

Mp 

Kl 

Np 

S 

SI 

M 

Kv 



Length of the AES key 

Minutiae point set 

key length 

Size of Minutiae pint set 

Seed value 

seed limit 

(x, y) -co -ordinate of a minutiae point 

Key Vector 

Step 1: The Extracted minutiae pints are 
represented 

As 

Mp={mi} i=l Np 

Step2: The initial key vector is defined as 
follows 

Kv= {xi:p(xi)}i=l Kl 

Where p(x) = Mp [1% Np] + Mp [(i+1) % Np] 

+ S 



i=l. 



.Kl 



step3: Initial value of S is equal to total 
Number of Minutiae pints . The value of S will 
be dynamically changed as follows 

S=Kv (i) %Sl,-l<i<Kl 

Step4: Initial key vector (Kv) is converted in to 
a 

Matrix Km of size Kl / 2 * Kl / 2 



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Km= (aij) Kl / 2 * Kl / 2 
Step 5: 
A intermediate key vector is generated as 

follows KIV= {Ki: (m (ki)} i=l Kl 

Where 

M (k) =IAijl, 

Aij=Km i, j: i + size + j + size, - 1 <i<kl/2 

Aij is a sub matrix formed from the key matrix 

Step 6: Final key vector is formed is 



Sc= 



-< 



l,ifKlV[i]>mean(KlV) 
0, other wise 



5. Conclusion 

From the above discussion I have proposed a 
method for providing security to the 
information transferring on the network using 
Encryption and a novel approach for 
fingerprint based cryptography system. Here 
we used fingerprint patterns, which are stable 
through out person's lifetime. Since it creates 
more complexity to crack or guess the crypto 
keys. This approach has reduced the 
complicated sequence of the operation to 
generate crypto keys as in the traditional 
cryptography system. 



6. References 



"t 



1. N.Lalithamani, K.P. Soman Irrevocable 
Cryptographic Key Generation from 
Cancelable Fingerprint Templates: An 
Enhanced and Effective Scheme". European 
Journal of Scientific Research ISSN 1450- 
216X Vol.31 No.3 (2009), pp.372-387 

2. P.Arul, Dr.A.Shanmugam "Generate a 
Key for AES Using Biometric For VOIP 
Network Security" Journal of Theoretical and 
Applied Information Technology 2009.107- 
112. 

3. "Advanced Encryption Standard "from 
http://en.wikipedia.org/wiki/Advanced_Encryp 
tion_ Standard 

4. Jain, A.K., Ross, A. and Prabhakar, S, 
"An introduction to biometric recognition," 
IEEE Transactions on Circuits and Systems for 
Video Technology, Vol. 14, No. 1, pp: 4- 20, 
2004. 

5. Umut Uludag, Sharath Pankanti, Salil 
Prabhakar, Anil K Jain "Biometric 
Cryptosystems Issues and Challenges" 
Proceedings of the IEEE 2004. 



6. Announcing the "ADVANCED 
ENCRYPTION STANDARD (AES)" -Federal 
Information, Processing Standards Publication 
197, November 26, 2001 

7. Jain, A.K.; Prabhakar, S.; Hong, L.; 
Pankanti, S., "Filter bank-based fingerprint 
matching", IEEE Transactions on Image 
Processing, vol. 9, no. 5, pp: 846-859, May 
2000, Doi:10.1 109/83.841531. 



Authors Profile 

Dr.R.Seshadri was born 

0^^^ in Andhra Pradesh, India, 

in 1959. He received his 

B .Tech degree from 

Nagarjuna University in 

1981. He completed his 

Masters degree in Control 

System Engineering from 

PSG College of 

Technology, Coimbatore in 1984. He was 

awarded with PhD from Sri Venkateswara 

University, Tirupati in 1998. He is currently 

Director, Computer Center, 

S.V.University, Tirupati, India. He published 

number of papers in national and international 

journals. At present 12 members are doing 

research work under his guidance in different 

areas. 



EkM 



Mr.T.RaghuTrivedi received 
MCA degree from Andhra 
University, Vizag He received 
his M.Tech in Computer 
Science from Nagarjuna 
University. He is a research 
scholor in S.V.University 
Tirupati, Andhra Pradesh. 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 



Classification of Five Mental Tasks Based on Two 
Methods of Neural Network 



Vijay Khare 1 

Jaypee Institute of Information Technology 

Dept. of Electronics and Communication, Engineering 

Nioda, India 

Email : vijay .khare@jiit.ac.in 

Sneh Anand 3 

Indian Institute of Technology, 
Centre for Biomedical Engineering Centre 
Delhi, India 
Email : sneh@iitd.ernet.in 



Abstract — In this paper performance of two classifiers based on 
Neural Network were investigated for classification of five 
mental tasks from raw Electroencephalograph (EEG) signal. Aim 
of this research was to improve brain computer interface (BCI) 
system applications. For this study, Wavelet packet transform 
(WPT) was used for feature extraction of the relevant frequency 
bands from raw electroencephalogram (EEG) signals. The two 
classifiers used were Radial Basis Function Neural Network 
(RBFNN) and Multilayer Perceptron Back propagation Neural 
Network(MLP-BP NN) . In MLP-BP NN five training methods 
used were (a) Gradient Descent Back Propagation (b) Levenberg- 
Marquardt (c) Resilient Back Propagation (d) Conjugate 
Learning Gradient Back Propagation and (e) Gradient Descent 
Back Propagation with movementum. 

Index Terms — Electroencephalogram (EEG), Wavelet Packet 
Transform (WPT), Radial Basis Function Neural 
Network(RBFNN), Multilayer Perceptron back propagation 
Neural Network(MLP-BP NN), Brain computer interfaces (BCI). 



I. 



Introduction 



Brain signals extracted through EEG carry information 
needed for the design and development of brain computer 
interface (BCI) systems. It is well documented that proper 
feature extraction and classification methods are the key 
features deciding the accuracy and speed of BCI systems [1- 
5]. ANN has been more widely accepted as one of the best 
classification method to distinguish various mental states from 
relevant EEG signals. 

Past two decades have witnessed the importance of innovative 
BCI with voice, vision and a combination of these, as a 
communication platform [6-9]. Effective attempts have been 
made to achieve successful BCI systems based on bioelectric 
signals. They were mainly to help patients with various 
neuromuscular disorders by providing them a way of 
communication to the world, through extracting information 
from their intensions. So far the accuracy of the classification 
has been one of the main pitfalls of the existing BCI systems, 
since it directly affects the decision made as the BCI output. 
The speed &accuracy could be improved by implementing 



Jayashree Santhosh 2 
Indian Institute of Technology, 
Computer Services Centre 
Delhi, India 
Email : jayashree@cc.iitd.ac.in 

Manvir Bhatia 4 

Sir Ganga Ram Hospital, 

Department of Sleep Medicine, 

New Delhi, India 

Email : manvirbhatial@yahoo.com 

better methods for feature extraction and classification [10- 
16]. In this study, wavelet packet transform (WPT) method 
was used to capture the information of mental tasks from eight 
channel EEG signals of nine subjects. The coefficients of 
wavelet packet transform (WPT) were used as the best fitting 
input vector for clssifiers [17]. The two classifiers (RBFNN 
and MLP-BP NN) were used to compare the performance to 
discriminate five mental tasks. 

II. METHODOLOGY 
A. Subjects 

Nine right-handed healthy male subjects of age (mean: 
23yr) having no sign of any motor- neuron diseases were 
selected for the study. A pro-forma was filled in with detail of 
their age & education level as shown in table 1. The 
participants were student volunteers for their availability and 
interest in the study. EEG data was collected after taking 
written consent for participation. Full explanation of the 
experiment was provided to each of the participants. 

Table 1 : Clinical Characteristics of Subjects 



S.No. 


Subject 


Age 


Educational 
status 


1 


Subject 1 


22 


BE 


2 


Subject 2 


21 


BE 


3 


Subject 3 


23 


BE 


4 


Subject 4 


27 


M.TECH 


5 


Subject 5 


23 


BE 


6 


Subject 6 


22 


BE 


7 


Subject 7 


27 


M.TECH 


8 


Subject 8 


22 


BE 


9 


Subject 9 


22 


BE 



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B. EEG Data Acquisition 



EEG Data used in this study was recorded on a Grass 
Telefactor EEG Twin3 Machine available at Deptt. of 
Neurology , Sir Ganga Ram Hospital, New Delhi. EEG 
recording for nine selected subjects were done for five mental 
tasks for five days. Data was recorded for 10 sec during each 
task and each task was repeated five times per session per day. 
Bipolar and Referential EEG was recorded using eight 
standard positions C3, C4, P3, P4, 01 02, and F3, F4 by 
placing gold electrodes on scalp, as per the international 
standard 10-20 system of electrode placement as shown in 
figure 1. The reference electrodes were placed on ear lobes 
and ground electrode on forehead. EOG (Electooculargram) 
being a noise artifact, was derived from two electrodes placed 
on outer canthus of left and right eye in order to detect and 
eliminate eye movement artifact. The settings used for data 
collection were: low pass filter 1Hz, high pass filter 35 Hz, 
sensitivity 150 micro volts/mm and sampling frequency fixed 
at 400 Hz. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
Data collected from nine subjects performing five mental tasks 
were analyzed. The following mental tasks were used to 
record the appropriate EEG data. 




Figure 1:- Montage for present study 
C. Experiment Paradigm 

An experiment paradigm was designed for the study and 
the protocol was explained to each participant before 
conducting the experiment. In this, the subject was asked to 
comfortably lie down in a relaxed position with eyes closed. 
After assuring the normal relaxed state by checking the status 
of alpha waves, the EEG was recorded for 50 sec, collecting 
five session of lOsec epoch each for the relaxed state. This 
was used as the baseline reference for further analysis of 
mental task. The subject was asked to perform a mental task 
on presentation of an audio cue. Five session of lOsec epoch 
for each mental task were recorded, each with a time gap of 5 
minute (as shown in figure2). The whole experiment lasted for 
about one hour including electrode placement. 



ft 



JHK 



ft 



JHK 



Mental 

Figure 2: Timing of the Protocol 



Sec. 



• Movement Imagination: -The subject was asked to 
plan movement of the right hand. 

• Geometric Figure Rotation: -The subject was given 30 
seconds to see a complex three dimensional object, 
after which the object was removed. The subject was 
instructed to visualize the object being rotated about 
an axis. 

• Arithmetic Task: -The subject was asked to perform 
trivial and nontrivial multiplication. An example of a 
trivial calculation is to multiply 2 by 3 and nontrivial 
task is to multiply 49 by 78. The subject was 
instructed not to vocalize or make movements while 
solving the problem. 

• Relaxed: - The subject was asked to relax with eyes 
closed. No mental or physical task to be performed at 
this stage. 

D. Feature Extraction 

The frequency spectrum of the signal was first analyzed 
through Fast Fourier Transform (FFT) method. The FFT plot 
of signals from the most relevant electrode pairs were 
observed along with average change in EEG power for each 
mental tasks as shown in figure (2-6). 

For relaxed, the peaks of power spectrum almost 
coincide (or difference of (0-10 %) for central and occipital 
area in the alpha frequency range (8-13Hz). EEG recorded 
with relaxed state is considered to be the base line for the 
subsequent analysis. Mu rhythms are generated over 
sensorimotor cortex during planning a movement. For 
movement imagery of right hand, maximum upto 50% band 
power attenuation was observed in contralateral (C3 w.r.t C4) 
hemisphere in the alpha frequency range (8-13Hz). For 
geometrical figure rotation, the peak of the power spectrum 
was increased in right hemisphere rather than left in the 
occipital area for the alpha frequency range (8-13Hz). For both 
trivial and nontrivial multiplication, the peak of the power 
spectrum was increased in left hemisphere rather than right 
hemisphere in the frontal area for the alpha frequency range 
(8-13Hz). 

The data was preprocessed using Wavelet packet 
transform to extract the most relevant information from the 
EEG signal. [18-19]. By applying Wavelet packet transform 
on the original signal wavelet coefficients in the (8-13Hz) 
frequency band at the 5 th level node (5, 3) were obtained. We 
were able to reduce 1 second of EEG data to 21 coefficients. 
The signal was reconstructed at node (5,3). These coefficients 
are scaled and used as the best fitting input vector for 
classifiers. 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 



Relax B-C4 R-C3 




Fig 2:- Power Spectra for Relax state at C3 and C4 channel 



Right Hand movement B-C4 R-C3 




g 3:- Power Spectra for planning of right hand movement at C3 and C4 



rotation B-02 R-Ol 




Power Spectra for visual rotation at Ol and 02 channel 



simple arthemetic B-F4 R-F3 




complex arithrnatic B-P4 R-P3 




Fig 6:- Power Spectra for complex Arithmetic P3 and P4 channel 

E. Classifier 

The main advantage of choosing artificial neural 
network for classification was due to fact that ANN's could be 
used to solve problems, where description for the data is not 
computable. ANN could be trained using data to discriminate 
the feature. We have compared two classifiers Radial Basis 
Function Neural Network (RBFNN) and Multilayer 
Perceptron Back propagation Neural Network (MLP-BP NN). 
In MLP-BP NN five training methods used were (a) 
Gradient Descent Back Propagation (b) Levenberg-Marquardt 
(c) Resilient Back Propagation (d) Conjugate Learning 
Gradient Back Propagation and (e) Gradient Descent Back 
Propagation with movementum. 

• Radial Basis Function Neural Network 

For this (RBFNN) classifier, a two layer network was 
implemented with 21 input vectors, a hidden layer consisting 
as many as hidden neurons as there are input vectors with 
Gaussian activation function and one neuron in the output 
layer [20-23]. RBFNN produce a network with zero error on 
training vectors. The output neuron gives 1 for a mental task 
and for relax task. 



• Multilayer 
Network 



Perceptron Back Propagation Neural 



For this classifier, a two layer feed forward neural 
network was used with topology of {10, 1}. 10 neurons in 
hidden layer and 1 neuron in output layer. The neural network 
was designed to accept a 21 element input vector and give a 
single output. The output neuron was designed to give for 
baseline (relax task) and 1 for mental task. The five different 
training methods used for this classifier were Gradient 
Descent, Resilient Back propagation, Levenberg-Marquardt, 
Conjugate Gradient Descent and Gradient Descent back 
propagation with movementum [24-27]. Parameter used for 
five training methods of neural network for classification of 
five mental tasks as shown in the table 2. 



Fig 5:- Power Spectra for Simple Arithmetic F3 and F4 channel 



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Table 2 
Parameter used for different algorithms 
with topology { 10, 1} 



Gradient descent with momentum (GDM) 


Topology {10,1} 


A=.01.Mu = 0.01 


MSE= le-5 




Epoach=5000 




Gradient descent method(GDBP) 


Topology {10,1} 


A=01 


MSE=lexp-(5) 




Epoach=5000 




Resilient Back propagation(RBP) 


Topology {10,1} 


A=01 


MSE=lexp-(5) 




Epoach=5000 


B=0.75 and p i=1.05 


Conjugate gradient descent(CGBP) 


Topology {10,1} 


A=01 


MSE=lexp-(5) 




Epoach=5000 




Levenberg-Marquardt(LM) 




Topology {10,1} 


Mu=01 


MSE=lexp-(5) 




Epoach=5000 


Mu_dec=0.1 and Mu_inc=10 



F. Performance 



The study evaluated the performance of two 
classifiers (RBFNN and MLP-BP NN) for classification of 
five mental tasks. For MLP-BP NN classifier five different 
training methods used were Gradient Descent, Resilient Back 
propagation, Levenberg-Marquardt, Conjugate Gradient 
Descent and Gradient Descent back propagation with 
movementum. 60% of entire EEG data (five sessions, five 
mental tasks with nine subject) was taken as training data. 
Remaining 40% of EEG data was taken as test data and the 
performances were recorded. The entire analysis of the 
recorded data was carried out using Matlab® 7.0 from 
Mathworks Inc., USA. 

Performance (Re) is calculated in percentage (%) as ratio 
between correctly classified patterns in the test set to the total 
number of patterns in the test set [28]. 



_ Number of correctly classified test patterns 
Total number of patterns in the test set 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 

classification of each mental task. WPT is an excellent signal 
analysis tool, especially for non stationary signals. Hence in 
the present study, WPT was used for feature extraction [29]. 

As per literature most prominent area in brain for domain of 
information during five mental tasks was shown in table3. 
Amplitude of power spectrum almost coincides in central and 
occipital area at a particular base frequency (8-13Hz) for 
relaxed states [12]. 

The frequency spectrum of the signal was observed 
that the amplitude of the power spectrum for alpha frequency 
range (8-13Hz) had an attenuation in contralateral area for 
movement imagery task [12 17]. 

For geometrical figure rotation, It was observed that 
the amplitude of the power spectrum increased in the right 
occipital region for alpha frequency range (8-13Hz) [30 31]. 

For trivial multiplication, it was observed that the 
amplitude of the power spectrum increased in the left frontal 
region for alpha frequency range (8-13Hz).For nontrivial 
multiplication, it was observed that the amplitude of the power 
spectrum increased in the left parietal region for alpha 
frequency range (8-13Hz) [31]. 



III. Result and Discussion 



Nine right-handed male subjects participated in the 
experiments. The subjects were asked to perform five mental 
tasks namely relaxed, movement imagery, geometrical figure 
rotation and arithmetic task (trivial and non trivial 
multiplication). Out of 50 sec data recorded data the most 
relevant one second epoch of signal were used for 



For MLP-BP NN classifier, Resilient back 
propagation training method showed better performance than 
other back propagation training methods (Gradient Descent 
method Levenberg-Marquardt, Conjugate Gradient Descent 
and Gradient Descent back propagation with movementum) 
for classification of five mental tasks (As shown in table 4). 



Table 3: Domain of information 



Tasks 


Domain of 
information 


(Contralateral/ 
Ipsilateral) 


Type of 

change in 

amplitude of 

alpha 

rhythm(8- 

13Hz) 


Base line 


Occipital, 
Central 


Contralateral 


Coincide 


Movement 
Imagination 


Central 


Contralateral 


Decreased 


Geometrical figure 
rotational 


Occipital 


Ipsilateral 


Increased 


Arithmetic' s(trivial 
and non trivial ) 
operation 


Frontal, 
parietal 


Ipsilateral 


Increased 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 



The present study was a comparison of two classifier, MLP- 
BP NN with Resilient back propagation training method and 
RBFNN to discriminate five mental tasks effectively. From 
figure(7-12) we can say that RBF Neural Network has better 
performance as compare to MLP-BP NN with Resilient back 
propagation method for classification of five mental tasks 
movement imagination(M), figure rotation(R), simple 
arithmetic (SA) task and complex arithmetic (CA) task, w.r.t 
baseline. Average accuracy was obtained 100% by using 
RBFNN classifier. 

IV. Conclusion 

For various applications of BCI systems, it is necessary that 
EEG feature related to the human intentions were to be 
uniquely identified as accurate as possible. In this paper, the 
two classifiers used were, MLP-BP NN with resilient back 
propagation training method and RBFNN. Radial basis 
function neural network was found to be most suitable for 
classification of five mental tasks. 

Radial basis function networks showed a better 
capability for solving larger data size problems at fast learning 
speed, because of their capability of local specialization and 
global generalization. Various other classification methods 
could be analyzed in future for better performance. 



Gradient Descent back Propagation 



Conjugate Gradient BP 



"5 98 

S,-96 

2 S 94 

§ o 92 
g re 

A* Qn 




































SA 




CA R 
Mental tasks 




M 





Fig 9: Classification accuracy CGBP training methods 



Gradient descent method with 
movementum 



II 



96 

94 
_ 92 

» 3 90 
| 8 88 

1 86 



t 



SA CA 



M 



Mental tasks 



•s 100 
95 
90 
85 
80 



o 






>» 


u> 


O 


O) 


<S 


(O 


:— 


+-» 


3 


c 


O 


CD 


O 


O 


<tt 


<D 




Q. 





















-. 



SA CA R M 

mental tasks 



Fig 7: Classification accuracy using GD BP training methods 



Fig 10: Classification accuracy GDM training methods 



Levenberg-Marquardt 



o 




96 


<D 


$ 


94 




92 


«5 


3 
O 


90 


O 


8 


88 


a> 




86 


a 







I r^ 



SA CA R M 

Mental tasks 

Fig 11: Classification accuracy LM training methods 



o 
= 3 

e 8 

Q. 



Resilient Back propagation 



98 i 
97 

96 
95 
94 
93 



SA CA R M 

Mental tasks 



o 

<D o 
O) to 

CO »- 

*- 3 

= o 

<D O 
£f < 
CD 
Q_ 


150 

100 

50 

n 


Radail Basis Function 






























SA CA R M 
Mental Task 



Fig 8: Classification accuracy RBP training methods 



Figure 12: Classification accuracy for RBFNN 



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Acknowledgment 



The authors would like to acknowledge their gratitude to the 
scientific and technical staff of EEG Laboratory of Sir Ganga 
Ram hospital, New Delhi for the help in carrying out the 
experiment. 

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Biographies 



Vijay Khare is currently pursuing his PhD in Bio Signal Processing at the 
Indian Institute of Technology, Delhi. He did his M.Tech in 
Instrumentation & Control, from NSIT Delhi. He is currently, 
with the Dept. Electronics and Communications Engineering at 
the Jaypee Institute of Information Technology. His research 
interests are Neural Networks, Brain Computer Interfacing, and 
Control Systems. 




Dr. Jayashree Santhosh completed her B.Tech in Electrical 
Engineering from University of Kerala, M Tech in Computer & 
Information Sciences from Cochin University of Science and 
Technology, Kerala and Ph.D from IIT Delhi. She is a Fellow 
member of IETE, Life member of Indian Association of Medical 
Informatics (IAMI) and Indian Society of Biomechanics (ISB). 
' ^" J **" Her research interests include IT in Healthcare Systems and was 
associated with a project on IT in Health Care at City University of Hong 
Kong. She is also associated with various projects with Centre for Bio- 
Medical Engineering at IIT Delhi in the area of Technology in Healthcare. 
Her research interests focus on Brain Computer Interface Systems for 
the Handicapped and in Neuroscience. 



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Prof. Sneh Anand is a professor and head, Center for 
Biomedical Engineering, Indian Institute of Technology, Delhi. 
She did B.Tech in Electrical Engg, from Punjab University, 
Patiala, and M.Tech in Instrumentation & Control from IIT 
Delhi and Ph.D. in Biomedical Engg. from IIT Delhi. Her 
research interests include biomedical instrumentation, 
rehabilitation engineering, biomedical transducers and 
Sensors. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
completed her MBBS in 1981, and Doctor of Medicine in 1986 from Christian 
Medical College and Hospital, Ludhiana. DM in Neurology 1993, from All 
India Institute of Medical Sciences. She is a member of Indian Academy of 
Neurology, Indian Epilepsy Society, Indian Sleep Disorders Association, 
World Association of Sleep Medicine, International Restless Legs Society 
Study Group and American Academy of Electrodiagnostic Medicine. Dr. 
Manvir Bhatia has been invited to deliver lectures in National & International 
workshops, conferences on topics related to Neurology, Epilepsy, Sleep 
Medicine and has sleep published papers in leading journals. 




Dr. Manvir Bhatia is the Chairperson of Dept. of 
SleepMedicine at Sir Ganga Ram Hospital, New Delhi and is 
also a Senior Consultant Neurologist.Dr. Manvir Bhatia 



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Sixth order Butterworth Characteristics using LV 
MOCCII and Grounded Components. 



T. Parveen 

Electronics Engineering Department, Z. H. College of Engineering & Technology 

AMU, Aligarh, INDIA 
tahiraparveen2@gmail.com 



Abstract — This paper introduces an active realization of the sixth 
order current mode Butterworth filter function using low 
voltage(LV) Multioutput current conveyors (MOCCII) and 
grounded passive components. The proposed realization is based 
on cascading an insensitive single input multi output (SIMO) 
current mode universal biquadratic filter (UBF). The UBF is 
constructed employing only two MOCCIIs, four grounded 
components, that lead to simple structure, easy to design and 
suitable for IC fabrication. The proposed UBF can realize all 
standard biquadratic responses without any matching conditions 
and has current outputs at a high impedance terminal, which 
enable easy cascadablity. An example of eighth order current 
mode Butterworth filter has been considered. The filter has the 
advantages of minimum requirement of active and passive 
component count, low sensitivity, and high performance. The 
performance of the filter is verified through PSPICE simulation 
using low supply voltage. 

Keywords-component; Current mode circuits, High order 
Butterworth filter, Multioutput current conveyors (MOCCIIs), 
Universal biquadratic filter. 



I. 



Introduction 



The current mode signal processing techniques have been 
received a wide attention due to its wide band width, improved 
linearity, wide dynamic range, low voltage operation as 
compared to voltage mode signal processing. By introducing 
Multi output current conveyors (MOCCIIs) in active filter 
designs, new more advantageous topologies [5] have been 
obtained, by which current outputs and current feedback can be 
developed when multiple current outputs are used [7]. 

Recently design of current mode universal biquadratic 
filters (UBF) with single input multi output (SIMO) have 
received considerable attention due to their convenience, high 
performance and greater functional versatility in terms of signal 
processing for practical applications [1-7]. Interconnections of 
relevant output currents provides the low pass, band pass, high 
pass, band elimination and all pass filter responses from the 
same circuit. 

This paper presents a minimum active-RC circuit 
realization of eighth order current mode Butterworth filter 
function using low voltage Multioutput current conveyors 
(MOCCII) and grounded Passive components. The proposed 
realization is based on cascading a novel single input multi 
output (SIMO) current mode universal biquadratic filter (UBF), 



which uses two Multi output current conveyors (MOCCII) 
along with only four grounded passive components. The 
presented UBF can realize the low pass, high pass, band pass, 
band elimination and all pass filter responses through 
appropriate selection of input and output terminals without any 
matching conditions. The filter circuit is simple in structure and 
has high impedance outputs enables easy cascading in current 
mode operations, and can be used for the realization of any 
type of high order filter function. The proposed UBF circuit 
also has the additional advantage of low component count, and 
high performance at low supply voltage, over previously 
reported literature [1-7, 10]. The eighth order current mode 
realization with Butterworth coefficients has not yet been 
reported. The design may be extended for maximally flat 
Butterworth characteristics by cascading another MOCCII 
UBF stages. 

II. Realization of universal biquadratic filter: 

The presented universal biquadratic filter uses only two 
MOCCIIs along with two grounded capacitors and two 
grounded resistors as shown in Figure 2. The Multi Output 
Current Conveyors (MOCCIIs) is shown in Figure 1. It is 
characterized by 



i y=° v x =v 



y hi =+h 



I = — I 



(1) 



where i= 1, 2, 3... 



Analysis of the circuit yields the following current transfer 
functions. 



1 



T LP (s) = 



T B p( S ) = 



l LP 



l IN 



l BP 



l IN 



7?j R 2 C x C 2 
D(s) 



D(s) 



(2) 



(3) 



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i - 


z + 

Y z~ 

z + 

MOCCII 

Z 

Z + 

z 


< lzl 


l y u 






< lzl 




. + 
M l z2 




< lzl 




+ 
< lz3 


h 






< lz3 







Figure 1. Symbol of MOCCII 
1 



S 2 +- 



T BE (s) = 



R x R 2 C x C 2 



± BE _ 

I UN ~ D(s) 



where, the denominator is given by: 
S 1 



D(s) = s z + 



R X C X 



■ + ■ 



7? 1 i?2^l^2 



(4) 



(5) 



From eqn. (2), (3) and (4) it is seen that inverting low pass, 
inverting band pass and non- inverting band elimination filters 
are realized at three outputs. Non- inverting high pass filter is 
realized just by connecting high impedance outputs I LP and I B e. 
And all pass filter is realized by connecting the high impedance 
outputs I B p and I B e. The realized high pass and all pass filter 
responses respectively are given by the following equations. 



T m (s) = ^- 



- HP y 



T Ap( S ) = 



1 UN 



1 AP 



l IIN 



D(s) 



RA 



■ + - 



R^R 2 CiC2 



D(s) 



(6) 



(7) 



The pole frequency co and the quality factor Q of the filters 
are given by 



CO, = 



1 



R^R 2 CiC 2 



Q = 



\ RA 

R 2 C 2 



(8) 



III. Realization of Butterworth higher order 
filters: 

Here we consider the realization of 6 th order Butterworth 
low pass filter by cascading three sections of current mode 
universal filter biquadratic filter (UBF) employing MOCCIIs 
shown in Figure 2. 

The normalized Butterworth transfer function for the 
resulting 6th order CM low pass filter is given by [9] 




z~ 

z; ] 

zr 3 



i 



Y, 


z; x 




MOCCII 


X 2 


Z 2~ 



Figure 2. Cascading stage (CM MOCCII UBF) for sixth order low pass 
Butterworth filter 

The transfer function can be de-normalized by replacing S — ► 
s/co to give the required sixth order filter function at given 
pole- co and pole-Q. The pole-Q of an individual biquadratic 
filter section is simply the reciprocal of the coefficients of s in 
eqn. (9) [8]. The values of pole-Q are 0.518, 0.707, and 1.932, 
respectively, for the three UBF to be cascaded. The UBF can 
be designed using these values of Q and for a given pole 
frequency. It is seen that no additional buffers are employed in 
the realization. The filter's pole frequency co and the quality 
factor Q of the current mode MO-CCII-based UBF are given 
by: 



co n = 



1 



R\R 2 C X C 2 



Q = 



R 2 C 2 



(10) 



IV. Non Ideal Effects 



Taking the non-idealities oil and Pi, i = 1 and 2, into 
consideration, the current transfer functions of the UBF are 
given then by: 

R.R^C.C^ 



T LP {s) = f L = ~ 



1 IN 



T BP( S ) = 



1 BP 



1 IN 



D\s) 
sa x ji x 



D\s) 



(11) 



(12) 



T be( s ) = 



■BE 



L IIN 



s i | cc x a 2 p x p 2 
R^R 2 C X C 2 



D'(s) 



(13) 



T(S) = —, : " : 

(s 2 + 1.932s + l)(s 2 + 1.414s + l)(s 2 + 0.518s + 1) 
The normalized pole frequency is at (o 0=h 



(9) 



HP 



T HP (s) = I m D'(s) 



(14) 



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1 AP 



R\C\ R X R 2 C X C 2 



T AP {s)J 



iin 



D'(s) 



(15) 



and 

D'(s) = s 2 + 



2 sa^/3^ a x a 2 /5 l /5 2 



R X C X 



- + ■ 



R X R 2 C X C 2 



(16) 



where the pole frequency (cb )and the quality factor (Q') of 
the filters obtained from D'(s) are given by: 



CO' = 



R X R 2 C X C 2 



Q' = 



\ RxC x a 2 P 2 
R 2 C 2 a x P x 



(17) 



At low to medium frequencies (f < 10 MHz), the circuit 
continues to provide standard second order responses. The 
pole- co is slightly lowered, but the pole-Q remains unaffected 
by the non- idealities. 

V. Sensitivity Study 

The sensitivity of filter parameters are evaluated with 
respect to active and passive elements and are given below. 



st° 



R l,, R 2,Ci,C 2 



S°', 



H^l^X^l 



.2 


Q — 

S R,,C ] ,a 2 J 2 = 2 


1 

2 


R 2 ,C 2 ,a } ,p Y ry 



(18) 



From the above calculation it is evident that the sensitivities 
of co and Q with respect to passive components are not more 
than half in magnitude. This shows the attractive sensitivity 
feature oftheUBF. 

VI. Design and simulation 

To demonstrate the performance of CM universal 
biquadratic filter , the circuit is simulated using PSPICE level 3 
parameters in 0.5um CMOS process with supply voltages V DD 
= -V ss = 0.75V, using MOCCII+ model derived from CCII 
[9]is shown in Figure 3. The dimensions of the MOS 
transistors of CCII are listed in Table 1. 



TABLE I. 



MOSFET Dimensions of the CCII. 



MOSFET 


W(//m) 


L(//m) 


Mi, M 2 


25 


0.5 


M 3 , M 9 


66 


1 


M 4 , M 6 , M 7 


4 


0.5 


M 5 


12 


0.5 


M 8 , M 10 


45 


1 



LVCU 
L L 



Hwfflk d^«„ \k |fc|fc 



n 



M 



i: 



K U 



i: v 



g \ Wt t h 3H&M&5 



LVCM 



Figure 3. CMOS Circuit for MOCCII 

Initially the UBF was designed for f = 1 MHz and Q = 
0.707. For Ci = C 2 = 1 1 pF, equation (8) yields R x = 10 K^, and 
R 2 = 20 Kf2. The simulated LP, BP, BE response of current 
mode UBF response is shown in Figure 4 shows good 
agreement with the theory. 

The frequency tuning of the BPF response at Q = 5 is next 
investigated by changing the center frequency f of the band 
pass filter through resistor R 2 . The BP response curves 
corresponding to/ = 300 KHz,/ = 500 KHz, and/ = 1MHz 
are given in Figure 5, which exhibit good agreement with the 
theory. 




30 KHz 100 KHz 300 KHz 1.0MHz 3.0MHz 1C 

d ILP/IIN d IHP/IIN nIBP/IIN IBE/ IIN a IAP/ IIN 

Frequency 

Figure 4. The simulated UBF response at J Q = 1 MHz 



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100 


Gain 








"\^ 


(DB) 




^\^ 


-100 




^\ 


-200 


ILP/IIN - — 


ILP/IIN 




Simulated 


Theoretical 



1.0MHz 
Frequency 



Figure 6. Frequency response of sixth order CM LPF in DB 



30 KHz 100 KHz 300 KHz 1.0 MHz 3.0 MHz 10M 

Frequency 

Figure 5. Frequency tuning of BPF at Q = 5 

Next we presents an example for the realization of 6 th order 
Butterworth low pass filter using low pass filter of UBF of 
Figure 2. To evaluate the performance of the sixth order 
Butterworth low pass filter, the circuit is designed for a pole 
frequency f =lMHz. The values of capacitors are selected 
equal for convenient in IC implementation and are assumed to 
be equal to 1 lpF. The resistors for each section are designed to 
satisfy the equation (8). The designed values for each section 
are given below. 

Section-I : R x 
0.518 



5.18 KQ, R 2 = 19.32 KQ, for pole Q 



Section-II : R x 

0.707 



7.076 KQ, R 2 = 14.15 KQ, for pole Q 



Section-Ill : R x = 19.31 KKQ, R 2 = 5.18 KKQ, for pole Q 
= 1.932 

It is seen that the simulated pole frequency of 1.04 MHz, is 
obtained from the simulation, which verifies the design. Figure 
6 gives the stop band attenuation of 120 DB/decade, verifying 
the 6 th order low pass response. Through the entire range, the 
simulated and theoretical responses overlap, showing close 
agreement with theory. 

The proposed circuit can also be used to realize other 
higher order responses, such as, band pass, high pass and band 
elimination filters, through a simple electronic switching 
arrangement, for selecting the desired response of the UBF. 
The result of the 6th order band pass filter is shown in Figure 7, 
with a simulated pole frequency/, =1.02 MHz and a pole-Q of 
1.84. The slopes below/) and above/ are each 60 DB/decade, 
thus verifying the 6 th order band pass response. At the pole 
frequency/ =1 MHz, the gain is equal to unity. 




Figure7. Frequency response of sixth order CM BPF in DB 



VII. Conclusion: 

The MOCCII based current mode universal biquadratic 
filter is used to realize sixth order Butterworth low pass filter 
by cascading biquadratic filter sections of UBF of Fig. 2, 
without using any additional current followers. With this 
cascade approach the realization of higher order filter is 
reduced to a much simpler realization of only second order 
filters. The proposed circuit uses grounded capacitors hence is 
suitable for IC implementation. The proposed circuit also has 
low sensitivity, low component count, at low supply voltage of 

± 0.75V. 

References 



[1] S. Ozoguz and C. Acar, "Universal current mode filter with reduced 
number of active and passive elements", Electron. Lett., Vol.33, pp. 948- 
949, 1997. 

[2] A.M. Soliman,"Current Conveyor filters: classification and review", 
Microelectronics. J., Vol.29, pp. 133-149, 1998. 

[3] A. Toker, and S. Ozoguz, "Insensitive current mode universal filter 
using dual output current conveyors,"Int. J. Electronics, Vol 87, No. 6 
pp.667-674, 2000. 

[4] H. Y. Wang and C. T. Lee, "Versatile insensitive current mode universal 
filter implementation using current conveyors", IEEE Trans. Circuits 
Sys.IL, Analog and Digital Signal Processing, Vol.48, No. 4, pp. 411- 
413,2001. 



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Vol 8, No. 3, 2010 



[5] E. Yuce, S. Minaei and O. Cicekoglu," Universal current mode active-C 
filter employing minimum number of passive elements", Analog Integ. 
Circ. Sig. Process., Vol.46, pp. 169-171, 2006. 

[6] J. W. Horng, C. L. Hou, C. M. Chang, J. Y. Shie and C. H. Chang," 
Universal current filter with single input and three outputs using 
MOCCIIs, Int. J. Electronics, Vol.94, No.4, pp.327-333, 2007. 

[7] N. Herencsar and K. Vrba, "Tunable Current-Mode Multifunction Filter 
Using Universal Current Conveyors", Third International Conference on 
Systems, ICONS 08, pp. 1-6, 2008. 

[8] R. Schaumann, M. E. Van Valkenburg, "Design of analog filters", 
Oxford University press, 2003 

[9] Rajput, S. S., Jamuar, S. S., "Advanced Applications of Current 
Conveyors: A Tutorial", J. of Active and passive Electronic Devices, 
Vol.2, pp.143-164, 2007. 

[10] M. Kumar, M.C. Srivastava, U. Kumar, "Current Conveyor Based 
Multifunction Filter", International Journal of Computer Science and 
Information Security, Vol. 7, No. 2, pp.104-107, 2010. 

[11] S.S. Rajput, and S.S. Jamuar, "A current mirror for low voltage, high 
performance analog Circuits", Analog Integrated Circuits and Signal 
Processing, Vol.36, pp 221-233, 2003. 

AUTHORS PROFILE 

Tahira Parveen received the B.Sc. 
Engineering and M.Sc. Engineering degrees 
from Z.H. College of Engineering & 
Technology, A.M.U. Aligarh, India in 1984 
and 1987 respectively. She has obtained her 
Ph.D degree in 2009 from Electronics 
Engineering Department, Z.H. College of Engineering & 
Technology, A.M.U. , Aligarh, India. She is currently an 
Associate professor in the department of Electronics 
Engineering, Z.H.College of Engineering & Technology, 
A.M.U. Aligarh, India. She has over 24 years of teaching and 
research experience. Her research interests are Electronic 
circuits and system design, Analog filters and Analog signal 
processing. She has published over 24 research papers in 
National and International Journals. 

She has reviewed a book on "Basic Electronics" of McGraw- 
Hill Publisher. She is a Fellow member of IETE (India). She 
received Vijay Rattan Award for outstanding services 
achievements & contributions. Her biography is published in 
Marquis Who's Who in the World (USA) 2007. She is 
Reviewer for the Journal: International Journal of Circuit 
Theory and Applications, National and International 
conferences. Her book entitled" A Textbook of Operational 
Transconductance Amplifier & Analog Integrated Circuits" is 
published by I.K. International publisher in 2009. 




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A Lightweight Secure Trust-based Localization 
Scheme for Wireless Sensor Networks 



P. Pandarinath 

Associate Professor, CSE, Sir C R 

Reddy College of Engineering 

Eluru-534001, Andhra Pradesh 

pandarinathphd@gmail.com 

sriram3 1 @ gmail.com 



M. Shashi 

Head of the Department, Department 

of CS&SE, Andhra University, 

Visakhapatnam- 530 003, 

Andhra Pradesh 
smogalla2000 @ yahoo.com 



Allam Appa Rao 
Vice Chancellor, 
JNTU Kakinada, 

Kakinada, 

Andhra Pradesh 

apparaoallam@gmail.com 



Abstract — Location based security plays an important role in the 
trustworthiness of wireless sensor networks and the results that 
are obtained from them. Enforcement of location-aware security 
policies requires trusted location information. As more of these 
location-dependent services get deployed, the mechanisms that 
provide location information will become the target of misuse and 
attacks. In this paper, we propose to design a protocol that 
validates the reliability of location information associated with 
event reports. The protocol depends on the collaborative 
interaction of the network nodes to find compromised nodes. 
Nodes in the network record information of routing paths taken 
by packets through the network. Upon receiving the route 
request packets from the nodes, the sink checks whether their 
route matches a historically expected behavior by packets from 
the same claimed location. A trust value for the location claim of 
this request is then created by the sink. The attached trust values 
will be used to certify the truthfulness of the packets location 
information. Since this scheme does not involve any complex 
cryptographic operations, it has less overhead and delay. By 
simulation results, we show that our proposed scheme attains 
good delivery ratio with reduced delay and overhead. 

Keywords- Localization; Wireless Sensor Networks; Trust; 
Security issues; Lightweight Secure Trust-based Localization 
(LSTL) 

I. Introduction 

A. Sensor networks and its applications 

Wireless Sensor Networks (WSNs) are a specific kind of 
ad hoc networks, highly decentralized, and without 
infrastructure. They are building up by deploying multiple 
micro transceivers, also called sensor nodes that allow end 
users to gather and transmit environmental data from areas 
which might be inaccessible or hostile to human beings. The 
transmission of data is done independently by each node, 
using a wireless medium. The energy of each node is limited 
to the capacity of its battery. The consumption of energy for 
both communication and information processing must be 
minimized [1]. 

Wireless sensor networks are an area of great interest to 
both academia and industry. They open the door to a large 
number of military, industrial, scientific, civilian and 
commercial applications. They allow cost-effective sensing 



especially in applications where human observation or 
traditional sensors would be undesirable, inefficient, 
expensive, or dangerous [2]. 

B. Localization in Sensor Networks 

In sensor networks, without earlier knowledge of their 
location, nodes are organized into an unintentional 
infrastructure dynamically. Localization or position estimation 
problem refers to the problem of finding the positions of all 
the nodes provided a few location aware nodes, relative 
distance and angle information between the nodes. The 
essential and vital problem in wireless sensor network 
operation is to determine the physical positions of sensors for 
following reasons. 

• It is always necessary to have the position information 
of sensors attached, in order to use the data collected 
by the sensors. For instance, in sensor networks, the 
physical location of each sensor should be known in 
advance for identifying the position of the detected 
objects in order to detect and track objects. 

• With the knowledge of the geographic positions of 
sensors, many communication protocols of sensor 
networks are built. In majority of cases, there is no 
supporting infrastructure available to locate the 
sensors as they are deployed devoid of their position 
information known in advance. 

Hence it is necessary to find the position of each sensor in 
wireless sensor networks after deployment [3]. 

C. Phases of Localization 

The localization phase is a very critical step that must be 
secured in order to ensure the integrity of the WSN and its 
associated services. 

• Firstly, this process allows the sensor to set up the 
necessary parameters to establish the paths that will 
lead their data towards end users. 

• The knowledge of their position is also an essential 
prerequisite for the final application that processes the 
data collected by sensors, i.e., the user needs to know 
the origin of collected data before using it. 



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• Finally, the end users might want to query some nodes 
by sending the position where information needs to be 
collected [1]. 

Many localization schemes have been proposed for sensor 
networks in recent years without depending on expensive GPS 
devices. The majority of existing schemes assume some 
unique nodes, called beacon nodes, which have the potential to 
know their own locations through either GPS receivers or 
manual configuration. The remaining nodes or the non-beacon 
sensor nodes can be equipped with somewhat cheap measuring 
devices for directionality, signal strength, or time of arrival, 
etc. The non-beacon nodes find their own locations using these 
measurements and the locations of three or more beacon 
nodes. This method is known as the beacon-based technique, 
which involves two stages in location discovery. 

i. A sensor node calculates its distances to each neighbor 
of the node using the received signal information. 

ii. With all these distance calculations, sensor nodes 
calculate the actual location of the node. [4] 

D. Security Issues on Localization 

An important concern for various applications of WSNs is 
the ability to validate the integrity of the sensor network as 
well as the retrieved data. Various types of security attacks 
include 

• The injection of false information into the regular data 
stream, 

• The alteration of routing paths due to malicious nodes 
advertising false positions (sink holes and worm 
holes), and 

• The forging of multiple identities by the same 
malicious node. 

Thus, location based security plays an important role in the 
trustworthiness of WSNs and the results that are obtained from 
them [5]. 

Enforcement of location-aware security policies requires 
trusted location information. As more of these location- 
dependent services get deployed, the mechanisms that provide 
location information will become the target of misuse and 
attacks. In particular, the location infrastructure will be 
affected by many localization- specific threats that cannot be 
tackled through traditional security services. Therefore, as we 
move forward with deploying wireless systems that support 
location services, it is sensible to integrate appropriate 
mechanisms that protect localization techniques from these 
new forms of attack [6]. 

The wormhole attack is a typical kind of secure attacks in 
WSNs. It is launched by two colluding external attackers 
which do not authenticate themselves as legitimate network 
nodes to other network nodes. One of the wormhole attackers 
overhears packets at one point in the network, tunnels them 
through the wormhole link to another point in the network, 
and the other wormhole attacker broadcasts the packets among 
its neighborhood nodes. This may cause a severe impact on 
the routing and localization procedures in WSNs [7]. 



E. Problems Identified and Proposed Scheme 

The proposed solutions to mitigate some of these 
localization attacks always involve traditional security 
techniques. But, it is unlikely that traditional security will be 
able to remove all threats to wireless localization. We 
therefore consider that instead of providing solutions for each 
attack, it is essential to achieve robustness to unpredicted and 
non-filterable attacks. Particularly, localization must function 
properly even in the presence of these attacks [6]. 

Digital signatures can prevent bogus seeds from injecting 
bogus location messages by authenticating seeds' 
transmissions to nodes. This could be done by distributing 
public keys corresponding to the seeds' private keys to each 
node before deployment. But public key encryption operations 
are often too computationally expensive for sensor nodes, 
however, and the long messages required drain power 
resources [8]. 

Another approach would be to use the mTesla protocol, by 
preloading each node with the initial hash chain value and 
each seed with the initial secret. This would save the expense 
of public key operations, but would delay localization until the 
next key in the hash chain is released. It would also require 
loose synchronization among the seeds [8]. 

Moreover, since distance measurements are susceptible to 
distance enlargement/reduction, such techniques may not be 
used to infer the sensor location [9]. 

In this paper, we propose to design a protocol that 
validates the reliability of location information associated with 
event reports. The protocol depends on the collaborative 
interaction of the network nodes to find compromised nodes. 
Each active node automatically has some knowledge of the 
activity within the network which can be used in determining 
the anomalous behavior. Nodes in the network record 
information of routing paths taken by packets through the 
network. Upon receiving a packet, nodes check whether their 
route matches a historically expected behavior by packets 
from the same claimed location. A trust value for the location 
claim of this packet is then created and propagated to the sink. 
The attached trust values will be used by the sink to certify the 
truthfulness of the packets location information. 

II. Related Work 

J. G. Alfaro, M. Barbeau and E. Kranakis [1] have 
provided three algorithms that enable the sensor nodes of a 
Wireless Sensor Network to determine their location in 
presence of neighbor sensors that may lie about their position. 
Their algorithms minimize the number of trusted nodes 
required by regular nodes to complete their process of 
localization. Also their algorithms always work for a given 
number of neighbors provided that the number of liars is 
below a certain threshold value, which is also determined. The 
three algorithms that they have presented guaranteed that 
regular nodes in the WSN always obtain their position 
provided that the number of liars in the neighborhood of each 
regular node is below a certain threshold value, which they 
determine for each algorithm. Their three algorithms allow the 
regular nodes to identify and isolate nodes that are providing 
false information about their position. Moreover, their 



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algorithms minimize the necessary number of trusted nodes 
required by regular sensors to complete their process of 
localization. They also guarantee a small exchange of data 
between nodes, minimizing in this manner the impact that the 
localization process has in terms of energy and battery life of 
sensors. 

Kaiqi Xiong and David Thuente [4] have proposed novel 
schemes for secure dynamic localization in sensor networks. 
Their proposed schemes can tolerate up to 50% of beacon 
nodes being malicious, and they have linear computation time 
with respect to the number of reference nodes. They also 
showed that their schemes are applicable and resilient to 
attacks from adversaries. They proposed several methods for 
secure location discovery in sensor networks which are 
developed through the technique of beacon-based localization. 

Honglong Chen et al [7] have investigated the impact of 
the wormhole attack on the localization and they proposed a 
novel consistency-based secure localization scheme against 
wormhole attacks, which includes wormhole attack detection, 
valid locator's identification and self-localization. They 
developed an enhanced identification approach which obtains 
better performance than the basic identification approach. 

Yanchao Zhang et al [10] have first analyzed the security 
of existing localization techniques. They then developed a 
mobility-assisted secure localization scheme for UWB sensor 
networks. They didn't intend to provide brand-new 
localization techniques for UWB sensor networks. Instead, 
they focused on analyzing and enhancing the security of 
existing approaches when applied in adversarial settings. In 
addition, they have proposed a location-based scheme to 
enable secure authentication in UWB sensor networks. 

Srdjan C apkun et al [11] have proposed a secure 
localization scheme for sensor networks based on the received 
signal strength (RSS) ranging techniques. Their scheme 
enables the network authority to obtain locations of sensor 
nodes in the presence of an attacker. Also their proposed 
scheme uses a small number of anchor nodes with known 
locations that provide points of reference from which the 
sensors locations are computed. Their scheme also makes use 
of robust localization and time synchronization primitives 
which, appropriately combined, enable the detection of attacks 
on localization, within a realistic attacker model. 

III. Proposed Lightweight Secure Trust-based 
Localization (LSTL) Scheme 

A. System Design and Overview 

Sensors will start generating event reports corresponding 
to their respective location, once the network becomes 
activated. A malicious sensor might then try to generate 
illegitimate event reports for locations other than its own. This 
can be detected when the nodes along the path from the source 
to destination, attaches trust values to passing data packets, 
ensuring the correctness probability of the declared source 
locations. These trust values evaluated based on gathered past 
traffic patterns combined with the claimed source location. 
When receiving packets, if any of the routing information 
diverges from projected traffic patterns, then the nodes have 
the chance to decrease the trust values associated with these 



packets. These decreased trust values replicate the appearance 
of an attacker in the routing pattern. 

We assume that the network at the start is free from 
attackers for a short period of time. The novelty in this scheme 
lies in the efficient way of shortening the history of traffic 
pattern and capability of using this history to authenticate the 
accuracy of future packets. 

As a part of the routing protocol, each sensor node will 
maintain a history and normalized count of each previously 
seen source destination pair for routed packets. New incoming 
packets from rarely seen sources will then be considered more 
suspicious and associated with a low trust value. 

B. Distance Estimation 

We first describe the meaningful way of comparing packet 
routes efficiently. Based on the sequence of nodes a packet has 
visited, we define a distance metric to measure the distance 
between the two paths. The distance is designed such that fake 
claimed locations for packets will result in large distances 
between real and expected paths. There are many generic ways 
to measure the distance between two curves in space. 

Given a path R , we take k samples {R\ , R2 , • • • , Rjc } on R. 

we define the distance between two paths R , R as the sum of 
squared distance between corresponding sample points. 



ri ( *'* ): 



k 
i=\ 



Ri - Ri 



(1) 



C. Trust Based Incentive Scheme 

An additional data structure called Neighbor's Trust 
Counter Table (NTT) is maintained by each network node. Let 
{TC1,TC2,'"} be the initial trust counters of the nodes 

{N\ , N2 •••} along the route from a source S to the sink D. 

Since the node does not have any information about the 
reliability of its neighbors in the beginning, nodes can neither 
be fully trusted nor be fully distrusted. When a source S wants 
to send data to the sink D, it sends route request (RREQ) 
packets. It contains the source and destination ids and location 
of the source and a MAC computed over the accumulated path 
with a key shared by the sender and the destination 

Each time, the sink estimates the distance between the two 

paths R and R towards the sink, using (1). If it is more than 
a maximum threshold Thl, then the trust counter value is 
decreased by a penalty of 5. 



(ie) TCi=TCi-S, 



(2) 



Then the NTT of node Ni is modified with the values of 
TC[ When the subsequent RREQ message reaches the 

destination, it checks the trust values of the intermediate 
nodes. The nodes are considered as well behaving nodes if the 
trust values are equal or greater than a trust threshold TC t ^ . 
On the other hand, the nodes are considered as misbehaving 



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nodes if the trust values are less than TC t ^ . Also nodes with 
trust values less than TC t ^ are prohibited from further 
transmissions. 

D. Route Discovery Process 

In the proposed protocol, once a node S want to send a 
packet to a sink D, it initiates the route discovery process by 
constructing a route request RREQ packet. It contains the 
source and destination ids and location of source and a MAC 
computed over the accumulated path with a key shared by 
each node. 

When an intermediate node receives two RREQ packets 
from the same claimed locations, it retrieves the corresponding 
path information from their respective MAC and appends its 
id to the path and recreates the MAC with a key which is 
shared with the destination. It then forwards the RREQ to its 
neighbors. 

Then the route request process is illustrated as below: 



* D 




Figure 1. Route Request Process 
From the figure 1 , we can see that there are two paths 

R: S ► D and 

R:~S * D 

for the sink D. 

For the path R , let N\ , N 2 , • • • , N4 be the intermediate 
nodes between S and D. Then the route request process of path 
R is given by 



RREQ: [S,D,MAC(S)] 



Ni 



RREQ: [S, D, MAC (S, NO, MAC(] 
Ni . ► N 2 



RREQ: [S, D, MAC (S, N, N 2 )] 

N 2 * N 3 



RREQ: [S, D, MAC (S, Ni N 2 Ni)] 

N 3 * N 4 



For the path R , let N\, N2,---N/\. be the intermediate 
nodes between S and D. Then the route request process of 
path R is given by 

RREQ: [S,D,MAC(S)] 
S ► Ni 

RREQ: [5,D,MAC(S,77i)] 

77] ► iVi 

RREQ: [S\ D, MAC (S , JVi, Ni)] 

77 2 ► Ns 



RREQ:[S,D,MAC(5,iVi,JV2,iV3)] 



W 3 



N . 



RREQ: [5, D, MAC (S,Ni r N 2 ,Ni t N4)] 



Na 



D 



When RREQs of both R and R reaches the sink, from the 
received MAC values, it calculates 



V = D(N h Ni) where 



D(N i ,Ni) = ^\\N i -N i II 2 by 



(1) 



i=\ 



Then it checks the value of V , based on which the trust 
values are increemented or decreemented for the 
corresponding nodes. 

IfV<tk[ then 

CCN t = CCN t + J, 
Else 

CCN t = CCN t - S, 
End if 

where ih\ is the minimum threshold value for V and S is 
the scale factor for increement or decreement. 

The process is repeated for various time intervals and 
finally the value of credit counter is checked, 

If CCNf > th 2 then 

RREP is sent 
Else 

The source is considered malicious, 

RREQ is discarded 
End if. 



RREQ: [S, D, MAC (S, N L N 2 N 3 N 4 )] 

N 4 + D 



Where th^ is the minimum threshold value for CCN 

In our scheme, only nodes which are stored in the current 
route need to perform these cryptographic computations. So 
the proposed protocol is efficient and more secure. 



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IV. Performance Evaluation 

A. Simulation Parameters 

We evaluate our Light weight Secured Trust based 
Localization (LSTL) Algorithm through NS2 simulation. We 
use a bounded region of 1000 x 1000 sqm, in which we place 
nodes using a uniform distribution. We assign the power levels 
of the nodes such that the transmission range and the sensing 
range of the nodes are all 250 meters. In our simulation, the 
channel capacity of mobile hosts is set to the same value: 2 
Mbps. We use the distributed coordination function (DCF) of 
IEEE 802.11 for wireless LANs as the MAC layer protocol. 
The simulated traffic is Constant Bit Rate (CBR). 

The following table summarizes the simulation parameters 
used 

TABLE I. Simulation Parameters 



No. of Nodes 


25,50,75 and 100 


Area Size 


1000 X 1000 


Mac 


802.11 


Simulation Time 


50 sec 


Traffic Source 


CBR 


Packet Size 


512 


Transmission Range 


250 


Routing Protocol 


AODV 


Speed 


5 


Mobility model 


Random way point 


Attackers 


5, 10, 15, 20 and 25 



B. Performance Metrics 

We compare the performance of our proposed LSTL with 
the SeRLoc [11]. We evaluate mainly the performance 
according to the following metrics: 

Average end-to-end delay: The end-to-end-delay is 
averaged over all surviving data packets from the sources to 
the destinations. 

Control overhead: The control overhead is defined as the 
total number of control packets exchanged. 

Estimation Error: It is the estimation error, which 
indicates how close the estimated location is to the actual 
location. 

Average Packet Delivery Ratio: It is the ratio of the 
number .of packets received successfully and the total number 
of packets transmitted. 

A. Based On Nodes 

In order to test the scalability, the number of nodes is varied as 
25, 50, 75 and 100. 



Nodes Vs Delay 




-LSTL 
-SeRLoc 



Figure2. Nodes Vs Delay 



Nodes Vs Overhead 




50 75 100 

Nodes 

Figure3. Nodes Vs Overhead 

Nodes Vs Error 




Figure4. Nodes Vs Error 

Figure 2 show the end-to-end delay occurred for both 
LSTL and SeRLoc. As we can see from the figure, the delay is 
less for LSTL, when compared to SeRLoc. 

Figure 3 shows the overhead for both LSTL and SeRLoc. 
As we can see from the figure, the overhead is less for LSTL, 
when compared to SeRLoc. 

Figure 4 shows the error occurred for both LSTL and 
SeRLoc. As we can see from the figure, the error is less for 
LSTL, when compared to SeRLoc. 

B. Based On Attackers 

The number of attacker nodes is varied as 5, 10, 15, 20 and 25 
in a 100 nodes scenario. 



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Attackers Vs Delay 


0.6 -i 










n r 


A^_ 






^ 








^ n ^ 






a> n o 


™ ™~^^2 


□ u.^ 






1 












o 












5 


10 15 20 25 






Attackers 



- SeRLoc 
-LSTL 



Figure5. Attackers Vs Delay 



Attackers Vs Overhead 




- SeRLoc 
-LSTL 



10 15 20 
Attackers 

Figure6. Attackers Vs Overhead 

Attackers Vs Error 




10 15 20 

Attackers 



Figure7. Attackers Vs Error 



Attackers Vs Delivery Ratio 




-LSTL 
- SeRLoc 



10 15 20 
Attackers 

Figure 8. Attackers Vs DelRatio 

Figure 5 show the end-to-end delay occurred for both 
LSTL and SeRLoc. As we can see from the figure, the delay is 
less for LSTL, when compared to SeRLoc. 



Figure 6 shows the overhead for both LSTL and SeRLoc. 
As we can see from the figure, the overhead is less for LSTL, 
when compared to SeRLoc. 

Figure 7 shows the error occurred for both LSTL and 
SeRLoc. As we can see from the figure, the error is less for 
LSTL, when compared to SeRLoc. 

Figure 8 shows the delivery ratio for both LSTL and 
SeRLoc. As we can see from the figure, the delivery ratio is 
high for LSTL, when compared to SeRLoc. 

V. Conclusion 

In this paper, we have designed a protocol that validates 
the reliability of location information associated with event 
reports. The protocol depends on the collaborative interaction 
of the network nodes to find compromised nodes. When a 
source S wants to send data to the sink D, it sends route 
request (RREQ) packets. It contains the source and destination 
ids and location of the source and a MAC computed over the 
accumulated path with a key shared by the sender and the 
destination. When the RREQ packets reach the sink, it 

estimates the distance between the two paths R and R 
towards the sink. If it is more than a maximum threshold Thl, 
then the trust counter value is decreased by a penalty of 5. 
When the subsequent RREQ messages reach the destination, it 
checks the trust values of the intermediate nodes. The nodes 
are considered as well behaving nodes if the trust values are 
equal or greater than a trust threshold TC t ^ . On the other 
hand, the nodes are considered as misbehaving nodes if the 
trust values are less than TC t ^ . Also nodes with trust values 

less than TC t ^ are prohibited from further transmissions. 

Since this scheme does not involve any complex cryptographic 
operations, it has less overhead and delay. By simulation 
results, we have shown that our proposed scheme attains good 
delivery ratio with reduced delay and overhead. 

References 

[1] J. G. Alfaro, M. Barbeau and E. Kranakis, "Secure Localization of 
Nodes in Wireless Sensor Networks with Limited Number of Truth 
Tellers", Proceedings of the Seventh Annual Communication Networks 
and Services Research Conference, 2009. 

[2] Eric Sabbah et al, "An Application Driven Perspective on Wireless 
Sensor Network Security", Proceedings of the 2nd ACM international 
workshop on Quality of service & security for wireless and mobile 
networks, 2006. 

[3] V. Vijayalakshmi et al, "Secure Localization Using Elliptic Curve 
Cryptography in Wireless Sensor Networks", IJCSNS International 
Journal of Computer Science and Network Security, VOL.8 No.6, June 
2008. 

[4] Kaiqi Xiong and David Thuente, "Dynamic Localization Schemes in 
Malicious Sensor Networks", Journal of Networks, Vol. 4, no. 8, 
October 2009. 

[5] E. Ekici et al, "Secure probabilistic location verification in randomly 
deployed wireless sensor networks", Ad Hoc Networks, Volume 6, Issue 
2, April 2008. 

[6] Zang Li et al, "Robust Statistical Methods for Securing Wireless 
Localization in Sensor Networks", Proceedings of the 4th international 
symposium on Information processing in sensor networks, 2005. 

[7] Honglong Chen et al, "A Secure Localization Approach against 
Wormhole Attacks Using Distance Consistency", EURASIP Journal on 
Wireless Communications and Networking, Volume 2010 (2010). 



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[8] Lingxuan Hu and David Evans, "Localization for Mobile Sensor 
Networks", Proceedings of the 10th annual international conference on 
Mobile computing and networking, 2004. 

[9] Loukas Lazos and Radha Poovendran, "SeRLoc: Robust Localization 
for Wireless Sensor Networks", ACM Transactions on Sensor Networks 
(TOSN),2005. 

[10] Yanchao Zhang et al, "Secure Localization and Authentication in Ultra- 
Wideband Sensor Networks", IEEE Journal on Selected areas in 
communications, 24(4), 829-835, 2006. 

[11] Srdjan C apkun et al, "Secure RSS-based Localization in Sensor 
Networks", Technical Reports 529, ETH Zurich, 09, 2006. 



a P. Pandarinath his B.Tech in CSE in 1994 and M.Tech in 

CSE in 2001. He is presently pursuing his doctorate. He 
has a total experience of 15 years. He is working as a 
Associate Professor in Sir C R Reddy college of 
Engineering. He has completed 80 projects and guided 100 
projects. His areas of interests include Network Security 
and Cryptography, Advanced Computer Architecture, 
DataBase Management System, Computer Organization, 
Computer Networks and Bio-informatics. 





Dr. M. Shashi completed her B.E. in EEE, and M. E. in 
Computers Engineering. She received Doctorate in 
Engineering. She is working as Head of the Department in 
Computer Science and Systems Engineering, Andhra 
University, Visakhapatnam. She has a total experience of 
24 years. She presented 17 international journals and 3 
national journals. She presented papers in 10 international 
conference and 8 national conferences. She attended 7 international and 25 
national conferences. She guided 85 M.Tech projects. She received AICTE 
Career Award for Young Teachers in 1996-1998 at National Level and 
Best Ph.D. Thesis Prize for 1994 & 1995 at University Level. Her areas of 
specializations include Data Warehousing & Mining, Al, Data Structures, Soft 
computing and Machine Learning. 




Dr. Allam Appa Rao received his B.Sc. (M.P.C.) in 1967 
from Andhra University. He completed M.A. in 
Economics: Mathematical Economics and Econometrics, 
from Andhra University. He received the Ph.D. in 
Computer Engineering from Andhra University, in 1984. 
He is working as Vice Chancellor for JNTU Kakinada. He 
has presented 150 research articles. Dr Allam holds two 
patents as a co inventor for Method(s) of stabilizing and potentiating the 
actions and administration of brain-derived neurotrophic factor (BDNF) patent 
Number 20080234197 dated 25th September 2008 ( Refer 
http://www.faqs.org/patents/inv/83318) and Method(s) of preventing, 
arresting, reversing and treatment of atherosclerosis Patent Number 
20080279925 dated 13th November2008(Refer 

http://www.faqs.org/patents/inv/171637). He achieved best Researcher in 
Engineering in recognition of commendable record of research in Engineering, 
Andhra University, Visakhapatnam, India, 2003. 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 008, No. 003, 2010 



Mechanism to Prevent Disadvantageous Child Node 

Attachment in HiLOW 



Lingeswari V Chandra, Kok-Soon Chai and 

Sureswaran Ramadass 

National Advanced IPv6 Centre, Universiti Sains Malaysia 

{ lingeswari , kschai, sures }@nav6.org 



Gopinath Rao Sinniah 

MIMOS Berhad, 57000 Kuala Lumpur 

gopinath. rao @ mimos.my 



Abstract — Vast research is being conducted in the area of 
Wireless Sensor Network in recent years due to it foreseen 
potential in solving problems covering many aspects of daily, 
industrial and ecological areas. One of the biggest challenges in 
the power and memory constrained sensor network is in 
establishing reliable network and communication among the 
nodes. . IP-based 6L0WPAN was introduced to give a new 
dimension to sensor network by enabling IPv6 to be applied to 
the wired as well as wireless sensors. An extendable and scalable 
Hierarchical Routing Protocol for 6L0WPAN (HiLOW) is one of 
three routing protocols which has been introduced specially for 
6L0WPAN. HiLOW was designed by exploiting the dynamic 16 
bit short addresses assignment capabilities featured by 
6L0WPAN. HiLOW clearly defines the network setup process, 
address allocation method and routing mechanism. However 
there are shortcomings or issues pertaining HiLOW that make it 
less efficient. One of the major issues identified in HiLOW is in 
the process of selecting the parent node to attach with during the 
network tree setup. Disadvantageous parent selection could lead 
to significant shorter life span of the network which affects the 
reliability and stability of the network. In this paper we review 
the HiLOW routing protocol, highlight the issues revolving 
HiLOW and suggest a mechanism to prevent disadvantageous 
child node attachment in HiLOW. The proposed mechanism 
takes into consideration the LQI value, the potential parents' 
depth in the network and the average energy level of the parent 
in selecting the suitable parent node in order to provide a more 
reliable wireless sensor network. 

Keywords- 6L0WPAN, routing protocol, HiLOW, WSN, 
Hierarchichal routing protocol 



I. 



Introduction 



Wireless Sensor Network (WSN) is an area which is being 
vastly researched on in recent years due to its foreseen 
capability in solving many existing problems, ranging from day 
to day problems, industrial problems and up to ecological 
problems. WSN initially started as a military network where it 
was used to detect enemies, land mines and identifying own 
man. Now WSN usage has been extended to general 
engineering, agriculture monitoring, environmental monitoring, 
health monitoring and also home and office monitoring and 
automation. 

The rapid growth and penetration of sensors to other areas 
are due to engineering contribution where more types of 
sensors has being developed and introduced while decreasing 
the size of sensor nodes, and power consumption and price of 



microprocessors while increasing the memory size of the 
nodes. Even though vast improvement has been witnessed from 
engineering perspective the nodes until today still faces the 
limitation in power and computational capacities and memory 
[1]. 

One of the most important elements after sensing activity 
but most energy costly element of the WSN is the 
communication part. Radio communication is typically the 
most energy consuming activities [2] and the reception energy 
is often as high as the transmission energy thus the network 
protocol introduced as well as the routing protocol needs to 
take into consideration the energy usage in setting up the 
network as well as complexity of computation during routing. 
A disadvantageous setup of network could lead to request of 
retransmission to occur and this would lead towards wastage of 
precious energy. 

Many communication protocols have been introduced to 
WSN prior to the introduction of 6L0WPAN namely 802.15.1 
Bluetooth [3], WirelessHart [4], ZWave [5], ZigBee [5] and 
others. Compared to the named communication network 
6LoWPAN[6] was the first to introduce IPv6 to be applied to 
not only wireless but also wired sensor network. 6L0WPAN 
defines the network layer and also the transport layer and is 
able to be deployed on any sensors which are IEEE 802. 15. 4 [7, 
8] compliant. The 6L0WPAN stack is 30KB minimum in size 
which is smaller compared to the named protocols. The routing 
protocol for 6L0WPAN is an open area where it is open to 
introduction of new protocols. Till today there are three 
prominent routing protocols which have been introduced 
specifically for 6L0WPAN namely Hierarchical Routing 
Protocol (HiLOW) [10, 11], Dynamic MANET On-demand for 
6L0WPAN (DYMO Low) [12] and 6L0WPAN Ad Hoc On- 
Demand Distance Vector Routing (LOAD) [13]. 

The remainder of this paper is organized as follows. Section 
2 reviews the HiLOW protocol in detail and the issues in it and 
works done to improve HiLOW. Section 3 suggests a 
mechanism to avoid a disadvantageous parent child attachment 
during the routing tree set up. Section 4 presents the 
conclusion. 

II. Related Works 

A hierarchical routing protocol (HiLow) for 6L0WPAN 
was introduced by K. Kim in 2007 [10]. HiLOW is a routing 
protocol which exploits the dynamic 16-bits short address 



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assignment capabilities of 6L0WPAN. An assumption that the 
multi-hop routing occurs in the adaptation layer by using the 
6L0WPAN Message Format is made in HiLOW. In the rest of 
this section we will discuss the operations in HiLOW ranging 
from the routing tree setup operation up to the route 
maintenance operation while highlighting issues revolving 
HiLOW. Other works done to solve some of the issues in 
HiLOW is also reviewed here. 



A. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 008, No. 003, 2010 
has same depth, same energy level and has different number of 
existing child. Their mechanism also does not take into 
consideration the quality of the link established between the 
parent node and child node. Therefore the suggested 
mechanism does not solve the arising issue completely. So in 
this paper we will be suggesting a mechanism to address this 
first issue by taking into consideration the link quality, the 
existing energy of the potential parent as well as the depth of 
the parent. 



HiLOW Routing Tree Setup, Issues and Works Done 

The process of setting up the routing tree in HiLOW 
consists of a sequence of activities. The process is initiated by a 
node which tries to locate an existing 6L0WPAN network to 
join into. The new node will either use active or passive 
scanning technique to identify the existing 6L0WPAN network 
in its Personal Operation Space (POS). 

If the new node identifies an existing 6L0WPAN it will 
then find a parent which takes it in as a child node and obtain a 
16 bit short address from the parent. The parent will assign a 16 
bit short address to a child by following the formula as in (1). 
An important element of HiLOW is that the Maximum 
Allowed Child (MC) need to be fixed for every network and all 
the nodes in the network is only able to accept child limited to 
the set MC. In the case where no 6L0WPAN network is 
discovered by the node then the node will initiate a new 
6L0WPAN by becoming the coordinator and assign the short 
address by 0. 



FC 



Future Child Node's Address 



MC : Maximum Allowed Child Node 

N : Number of child node inclusive of the new node. 

AP : Address of the Parent Node 



Second issue revolves around the MC value which is being 
fixed for all nodes. The current scenario works well if all the 
nodes have the same power conservation method; meaning if 
all the nodes in the network are either battery powered or 
mains-powered. In the case where some nodes are battery 
powered and the others are mains-powered, then this method is 
not advantageous. The main-powered nodes could support 
more nodes as their child node as they are affluent in energy. 
This is an open issue to be addressed in HiLOW, for present 
time the assumption that all nodes having same energy 
conservation have to be made. The activity of disseminating the 
MC value to joining nodes is also left in gray. This issue is not 
addressed in this paper. 

B. Routing Operation in HiLOW 

Sensor nodes in 6L0WPAN can distinguish each other and 
exchange packet after being assigned the 16 bits short address. 
HiLOW assumes that all the nodes know its own depth of the 
routing tree. The receiving intermediate nodes can identify the 
parent's node address through the defined formula (2). The '[]' 
symbol represents floor operation 



AC : Address of Current Node 
MC : Maximum Allowed Child 



FC = MC * AP + N ( < N <= MC ) 



(1) 



AP = [(AC-1)/MC] 



(2) 



Two potential issues have been identified in this process. 
First issue would be when the child node gets respond from 
more than one potential parent. There is no clear mechanism 
rolled out in selecting the suitable parent to attach with. If the 
new node chooses to join the first responding parent node, it 
could be bias to the parent as some parent might be burdened 
with more parents meanwhile other parents which is in the 
same level has less child or none at all. Selecting the parent 
based on first responded potential parent could also lead to fast 
depletion of energy to certain parent causing the life span of the 
network to be shorter and the stability to be jeopardized. 
Selection of parent without considering the link quality could 
cause towards high retransmission rate which will consume 
energy from the child node as well as parent node. 

In [15] a mechanism to overcome the issue was suggested. 
Their mechanism suggests the potential parent node to provide 
the new child with its existing child node count 
(child_number). By issuing the child_number the node could 
select suitable parent which has less child nodes. The suggested 
mechanism performs well only when the potential parent node 



The receiving intermediate nodes can also identify whether 
it is either an ascendant node or a descendant node of the 
destination by using the above formula. When the node 
receives a packet, the next hop node to forward the packet will 
be calculated by the following three cases (3) which is defined 
in [10]. No issues have been identified so far in this process. 



S A : Set of Ascendant nodes of the destination node 

SD : Set of Descendant nodes of the destination node 

AA(D,k): The address of the ascendant node of depth D of the 
node k 

DC : The depth of current node 

C : The current node 

Case 1 : C is the member of S A 



(3) 



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The next hop node is A A (DC+1, D) 



Case 2: C is the member of SD 



The next hop node is AA (DC-1, C) 



Case 3 : Otherwise 



The next hop node is AA (DC-1, C) 



C. Route Maintenance in HiLOW 



Each node in HiLOW maintains a neighbor table which 
contains the information of the parent and children node. 
When a node loses an association with its parent, it should to 
re-associate with its previous parent by utilizing the 
information in its neighbor table. In the case of the association 
with the parent node cannot be recovered due to situation such 
as parent nodes battery drained, nodes mobility, malfunction 
and so on, the node should try to associate with new parent in 
its POS [11]. Meanwhile if the current node realizes that the 
next-hop node regardless whether its child or parent node is not 
accessible for some reason, the node shall try to recover the 
path or to report this forwarding error to the source of the 
packet. 

Even though a route maintenance mechanism has been 
defined in HiLOW, the mechanism is seen as not sufficient to 
maintain the routing tree. An Extended Hierarchical Routing 
Over 6L0WPAN which extends HiLOW was presented by in 
[16] in order to have better maintained routing tree. They 
suggested two additional fields to be added to the existing 
routing table of HiLOW namely, Neighbour_Replace_Parent 
(NRP) and Neighbour_Added_Child (NAC). This NRP doesn't 
point to the current parent node but to another node which can 
be its parent if association to current parent fails. Meanwhile 
NAC refers to the newly added child node. More work need to 
be done on this mechanism on how many nodes allowed to be 
adapted by a parent node in addition to the defined MC and 
whether this mechanism will have any impact on the routing 
operation, however this topic is beyond the scope of this paper. 

III. DlSADVANTEGOUS CHILD NODE ATTACHMENT 
AVOIDANCE MECHANISM 

A disadvantageous child parent attachment avoidance 
mechanism for HiLOW is being suggested in this paper. The 
suggested mechanism is able to overcome the bias child node 
phenomena that could shorten the life span of the network as 
well as affect the reliability and the stability of the network. We 
are suggesting a mechanism where the new child node is 
provided with three data; one is the Link Quality Indicator 
(LQI) value, secondly the depth of the potential parent node 
and thirdly the average amount of energy the potential parent 
node has. The suggested mechanism is an enhancement work 
of the mechanism suggested in [17]. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 008, No. 003, 2010 
LQI value can be measured by either parent node or by 
child node itself. The prior measurement by potential parent 
node was more preferred compared to the latter which is 
measurement by child. The measurement by child was not 
selected as the child node needs to use energy to measure LQI 
for every potential parent node and this would make the total 
energy usage to be higher compared to energy used when 
parent node measures the LQI only for one time. 

Therefore in this mechanism the LQI value will be 
measured by the potential parent node and the value is provided 
to the new node which is looking for a parent node; this is in 
contrast to the mechanism suggested in [18]. LQI is selected 
compared to Received Signal Strength Indication (RSSI) as 
LQI is more accurate to measure the quality of the link and the 
delivery ratio especially when obstructions or noise exist [18]. 
In previous mechanisms quality of the link is not considered in 
selecting the parent node. The quality of the link is important in 
making association as bad links would cause retransmission of 
data to occur and this causes nodes to use more energy. 



The node which is able to accept the new node as child 
node will calculate the average amount of energy it has 
according to the mathematical equation in (4) which is defined 
earlier in [17]. The average amount of energy represents the 
energy the potential parent node can equally use for itself as 
well as use to forward the data of existing child nodes and 
potential child. So the value 2 in the equation represents itself 
and the potential new child node. 



Avg : Average Amount of Energy 

CBP : Current Energy Level of the Potential Parent 

EC : Existing Child Node 



Avg= CBP / (EC+2) 



(4) 



In situation where there is more than one potential parent 
the child node will then make decision on which potential 
parent node to associate with according to steps as displayed in 
Fig. 1. First the child node will calculate the average LQI based 
on the equation (5). The '[]' symbol represents floor operation 
The average is calculated by summing up all the LQI value 
received, then dividing it by the number of parent node which 
responded and lastly flooring the value . In [18] a threshold was 
to be set and the LQFs will be compared to this threshold, this 
method needs intervention from human in determining the 
threshold and a method to communicate the threshold to all 
nodes; the communication process will again consume energy, 
due to this factor the new method is introduced. 



ALQI : Average LQI of all the potential links 
TLQI : Total LQI of all the potential links 
Count : Count of potential parent node 



ALQI= [TLQI / Count] 



(5) 



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>1 pp 



Average out all 
the PPs' LQI 



PPs above or equal 
to the average LQI 




PPs having lowest 
depth 



, Select the PP with\ 
Only 1 parent »( the hjghest 

average energy 



with the highest 
average energy 



PPs Having Highest 
Average Energy 




with highest 
LQI 



/Select PP with the \ 
highest LQI 



PPs Having Highest 
Level of LQI 



/ Select the first PP which ■ 
v responded , 



Legend : 

PP - Potential Parent Node 

PPs - Potential Parent Nodes 



Figure 1 . Parent Node Selection Mechanism 

The child node then compares all the potential parents' LQI 
with the average LQI calculated. In the case only one parent 
node's LQI is higher than average LQI then it would be 
selected as parent node. If there is more than one parent above 
the average LQI, then the child node will compare the depth of 
all the potential parent nodes which is above the average. If 
there is only parent node with the lowest depth, then that 
particular parent node is selected and process of associating is 
started. In the case where there is more than one potential 
parent with the lowest level of depth, then the average energy 
of these nodes are compared. The child selects the parent with 
the highest average energy and associate with it if there is only 
one particular potential parent with highest energy. In a 
situation where more than one parent shares the highest level of 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 008, No. 003, 2010 
energy and lowest level of depth; the child node will then 
compare the LQI value of these nodes. The node with highest 
LQI value will be selected as parent node to be associated with 
if only one node qualifies. If more than one parent has the 
lowest level of depth, highest level of energy and highest level 
of LQI then the child node will try to establish association with 
the first responded potential parent in this category. In [12] the 
LQI value is not considered when more than one parent shares 
the highest level of energy and lowest level of depth, the child 
straight away selects the first responded parents which is not 
advantageous if the first responded parent has lower LQI 
compared to the other potential parent. 

The first scenario will be described with the assistance of 
Fig. 2. When three potential parent node(8), node(4) and 
node(3) responds; according to the previous mechanism the 
child node should attach to parent node(3) as it has no child 
node compared to node(4) which has 2 child node and node(8) 
which has 1 child node. According to our mechanism the child 
will not consider the node(3) for attachment as the link quality 
is below the average, this represent the quality is bad compared 
to node(4) and node(8) and this could lead to high 
retransmission rate compared to node(4) and node(8) link. If an 
assumption that the node(3) has also two child nodes is made, 
then according to the previous mechanism the node(8) will be 
selected as suitable parent and this is also disadvantageous. 
Meanwhile according to the suggested mechanism in both 
scenarios where the node(3) has none or two child nodes, 
node(4) will still be selected as the parent node as it above the 
average LQI and it only goes through 1 hop to sink node 
compared to 2 hops if attached to node(8). By attaching to 
node(4), only node(X)'s and node(4)'s energy will be used in 
transmitting the data to the sink node, meanwhile if attachment 
in made to node(8) then energy of node(X)'s, node(8)'s and 
node(l)'s will be consumed in transmitting the data to the sink 
node. 

The second scenario is when there are two or more 
potential parent nodes with different number of existing child 
nodes as represented in Fig. 4. According to the previous 
mechanism the node(X) should associate itself with node(17) 
or node(3) based on which node responded first as both nodes 
have no child node compared to node(8) which has one child 
node. In the case the first responding parent is node(3) then it is 
disadvantageous if the child join node(3) as the link quality is 
not good compared to node(17). Meanwhile if the first 
responded parent node is node(17) the child will choose 
node(17) to attach with. This attachment is acceptable if all the 
nodes have same level of energy. In the case node(8) has 
abundant amount of energy compared to node(17) then this 
association is disadvantageous. Our mechanism suggests the 
node(X) to take into consideration the average amount of 
power the potential parent node has. In the case parent node(8) 
has more average power than parent node(17) then node(X) 
will join parent node(8). Meanwhile if the parent node(17) has 
more power then it will attach itself with parent node(17). 

The third scenario is when all potential parents have the 
same number of child nodes. The previous mechanism didn't 
anticipate such a situation will occur. Following our 
mechanism node(X) will first compare potential parent depth 
from sink node in this case node(4), node(8) and node(17) (Fig. 



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Vol. 008, No. 003, 2010 



3). In this case the node(X) will try to associate with node(4) 
as it nearer to the sink node and will not consider node(3) has 
LQI below average. Meanwhile if the potential parent node 
above average which responded is only node(8) and node(17), 
node(X) will join the node which has highest average energy. 
In the case both node(8) and node(17) has the same amount of 
average energy it will associate itself with the node which has 
highest LQI. In the case node(8) and node(17) has the same 
depth, same average energy and same level of LQI then 
node(X) will attach to the first potential parent which 
responded. 



Depth 1 




Legend 







Potential Parent with LQI below Average LQI 

Potential Parent with LQI above or equal to 
Average LQI 



Figure 2. Two Potential Parent Node with LQI above average, with different 
depth level and different number of existing child node 



Depth 



Depth 1 



Depth 2 



Depth 3 




Legend 



Potential Parent with LQI below Average LQI 

Potential Parent with LQI above or equal to 
Average LQI 



Figure 3. Two potential Parent node with LQI above average and with same 
depth level but different number of existing child node 



Depth 



Depth 1 



Depth 2 



Depth 3 




Legend 



Potential Parent with LQI below Average LQI 

Potential Parent with LQI above or equal to 
Average LQI 



Figure 4. Potential parents with same number of child nodes with some from 
different depth and some sharing same depth 



IV. Conclusion 

We provides review on HiLOW routing protocol, issues 
revolving HiLOW and other works done in improving HiLOW. 
In this paper we have suggested a new mechanism to overcome 
disadvantageous parent and child node attachment in HiLOW; 
disadvantageous attachment could jeopardize the reliability of 
the network, shorten the life span of the network and also cause 
wastage of energy due to retransmission. Previous mechanism 
used seems to be less advantageous as it is not considering 
potential link quality, the potential parent depth and also the 
energy level. To verify our mechanism, we identify a number 
of scenarios that need to optimize LQI, average energy level 
and parent-child nodes parameters. By analyzing these 
scenarios, the proposed new mechanism is shown to optimize 
the parameters better that leads to establishing of a more 
reliable network and enhancing the lifetime of the network. 
However, the implementation of the proposed mechanism in an 
actual network is a future work. Our future research will be 
focused on validating the suggested mechanism as well as 
solving the other issues highlighted in this paper. 

Acknowledgment 

The author would like to acknowledge Universiti Sains 
Malaysia (USM) for funding of USM Fellowship Scheme 
2009/10. 



References 

[1] Ian F.Akyildiz, Weilian Su, Yogesh Sankarasubramaniam and Erdal 
Cayirci, "A Survey on Sensor Networks", Communication Magazine, 
IEEE, Volume 40 

[2] A. Dunkels, F. "Osterlind, N. Tsiftes, and Z. He. Software-based online 
energy estimation for sensor nodes. In Proceedings of the Fourth IEEE 



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Workshop on Embedded Networked Sensors (Emnets IV), Cork, 
Ireland, June 2007. 

[3] L.Martin, B.D Mads, B.Philippe "Bluetooth and sensor networks: a 
reality check", Proceedings of the 1st international conference on 
Embedded networked sensor systems, 2003 

[4] S.Jianping, et al., "WirelessHART: Applying Wireless Technology in 
Real-Time Industrial Process Control", In Proceedings of IEEE Real- 
Time and Embedded Technology and Applications Symposium, 2008. 

[5] B.Chiara, C.Andrea, D.Davide, V.Roberto, "An Overview onWireless 
Sensor Networks Technology and Evolution", Sensors 2009, Sensors 
2009, 9, 6869-6896; doi:10.3390/s90906869 

[6] N. Kushalnagar, et al., "Transmission of IPv6 Packets over IEEE 
802.15.4 Networks", rfc4944, September 2007. 

[7] IEEE Computer Society, "802. 15.4-2006 IEEE Standard for Information 
Technology- Telecommunications and Information Exchange Between 
Systems- Local and Metropolitan Area Networks- Specific 
Requirements Part 15.4: Wireless Medium Access Control (MAC) and 
Physical Layer (PHY) Specifications for Low-Rate Wireless Personal 
Area Networks (WPANs)" 

[8] K. Kim, S.Yoo, S.Daniel, J.Lee, G.Mulligan, "Problem Statement and 
Requirements for 6L0WPAN Routing", draft-ietf-61owpan-routing- 
requirements-02, March 2009 

[9] K. Kim, S.Yoo, S.Daniel, J.Lee, G.Mulligan, "Commisioning in 
6L0WPAN", draft-61owpan-commisioning-02, July 2008 

[10] K. Kim, et al., "Hierarchical Routing over 6L0WPAN (HiLOW)", draft- 
daniel-61owpan-hilow-hierarchical-routing-01, June 2007. 

[11] K. Kim, et al., "Hierarchical Routing over 6L0WPAN (HiLOW)", draft- 
daniel-61owpan-hilow-hierarchical-routing-00, June 2005. 

[12] K. Kim, G.Montenegro, S.Park, I.Chakeres, C.Perkins, "Dynamic 
MANET On-demand for 6L0WPAN (DYMO-low) Routing", draft- 
montenegro-61owpan-dymo-low-routing-03, June 2007. 

[13] K.Kim, S.Daniel, G.Montenegro, S.Yoo, N. Kushalnagar, "6L0WPAN 
Ad Hoc On-Demand Distance Vector Routing (LOAD", draft-daniel- 
61owpan-load-adhoc-routing-02, March 2006 

[14] Martin Haenggi, "Opportunities and Challenges in Wireless Sensor 
Network", Sensor Network Protocol, Taylor & Francais Group pp. 1- 
1,1-7. 

[15] Hun-Jung-Lim, Tai-Myoung Chung, "The Bias Routing Tree Avoiding 
Technique for Hierarchical Routing Protocol over 6L0WPAN", 2009 
Fifth International Joint Conference on INC, IMS and IDC. 

[16] C.Nam, H.Jeong, D.Shin, "Extended Hierarchical Routing Protocol over 
6LowPAN", MCM2008, September 2008. 

[17] V.C.Lingeswari et al., "Bias Child Node Association Avoidance 
Mechanism for Hierarchical Routing Protocol in 6L0WPAN", 
Proceedings of the Third IEEE International Conference on Computer 
Science and Information Technology (In Press) 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 008, No. 003, 2010 
[18] Zhu Jian, Zhao Lai, "A Link Quality Evaluation Model in Wireless 
Sensor Networks", Proceedings of the 2009 Third International 
Conference on Sensor Technologies and Applications. 





AUTHORS PROFILE 

Lingeswari V.Chandra was born in Penang, 
Malaysia. She obtained her BIT with Management degree 
from AIMST University in 2008. She is the university gold 
medalist. She obtained her software engineering foundation 
training from Infosys, Bangolore. She is currently pursuing 
her PhD in National Advanced IPv6 Center, Universiti 
Sains Malaysia. 



Kok-Soon Chai was born in Penang, Malaysia. He is 
a certified Project Management Professional, Project 
Management Institute, USA. He received his MSc and 
Ph.D. (2003) degrees from the University of Warwick, UK. 
He worked for more than seven years as a senior R&D 
software engineer, embedded software manager, and CTO 
at Motorola, Agilent, Plexus Corp., Wind River in 
Singapore (now a division of Intel Corp.), and 
NeoMeridian. He holds one US patent, with two US patents pending. His 
main interests are wired and wireless sensor networks, green technology, 
embedded systems, consumer electronics, and real-time operating systems. 
Dr. Chai is a senior lecturer at the National Advanced IPv6 Centre of 
Excellence (NAV6) in Universiti Sains Malaysia. 



^^^ Gopinath Rao Sinniah obtained his BComp Science 

jfi ^k and MSc (Comp Science) from the Universiti Sains 

Malaysia in 1999 and 2004 and currently pursuing his 

Ph.D at the same university. He has involved in the IPv6 

development work since 1999 and currently working at 

^^^ MIMOS Berhad as Senior Staff Researcher focusing on 

df~k\ IPv6 specifically on wireless sensors network. Currently he 

^^^^^^^™ holds 10 patents filed locally and 1 at WIPO. He is also the 

chairman of MTSFB IPv6 working group. 



Sureswaran Ramadass obtained his BsEE/ce 
& (Magna Cum Laude) and Master's in Electrical and 
Computer Engineering from the University of Miami in 
1987 and 1990, respectively. He obtained his Ph.D. from 
Universiti Sains Malaysia (USM) in 2000 while serving as 
^^■j a full-time faculty in the School of Computer Sciences. Dr. 
^k Sureswaran Ramadass is a Professor and the Director of 
the National Advanced IPv6 Centre of Excellence (NAV6) 
in Universiti Sains Malaysia. 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 



Rough Entropy as Global Criterion for Multiple DNA 

Sequence Alignment 



Sara El-Sayed El-Metwally 

Dept. of Computer Science 

Faculty of Computer and Information Science 

Mansoura, Egypt 

Sarah_almetwally4@vahoo.com 



ElSayed Radwan Taher. Hamza 

Dept. of Computer Science Dept. of Computer Science 

Faculty of Computer and Information Science Faculty of Computer and Information Science 

Mansoura, Egypt Mansoura, Egypt 

elsfradwan @ yahoo, com Taherjiamza @ yahoo, com 



Abstract-This paper presents a new method for multiple sequence 
alignment using rough entropy as a global criterion to measure 
the quality of alignment. This method collects DNA sequences in 
clusters based on rough sets indcernibility relation. Rough 
entropy is used to maximize the total number of sequences inside 
each cluster with respect to the total number of sequences being 
aligned. The method terminates when all aligned sequences are 
located in all clusters and hence the problem is near optimally 
solved. 

Keywords-Multiple Sequence Alignment MSA, Rough Set 
Theory, Indcernibility relation, Rough Entropy, Similarity relation 

SIM(P),S P (x). 

I. Introduction 

Multiple Sequence Alignment (MSA) is a fundamental and 
challenging problem in computational molecular biology. It 
plays a key role in computing similarity and in finding highly 
conserved subsequences among set of DNA sequences .MSA is 
one of the most commonly used methods for inferring 
biological structures and functions. Moreover, it is the first 
step of many tasks in computational biology involving 
fragment assembly, evolutionary tree reconstruction, and 
genome analysis. 

For pairwise alignment, algorithms such as Needleman- 
Wunsch and Smith- Waterman [1] which use dynamic 
programming approach, guarantee finding the optimal solution 
[2] .For multiple alignments, methods such as 
multidimensional and repeating pairwise dynamic 
programming, try to find near optimal solution in reasonable 
amount of time. Finding optimal solution in reasonable 
amount of time becomes difficult when the number of 
sequences and the length of each sequence increase. The 
problem of computing minimum cost for MSA has been shown 
to TVT-hard. As a result, all multiple alignment algorithms 
currently in use depend on different kinds of heuristics [2, 4, 7, 
and 8]. 

The most common current solution to multiple DNA sequence 
alignment problem is to use an evolutionary computation (EC) 
where genetic algorithms find better alignment of sequences 



with better similarity score by Darwin's theory about 
evolution. Genetic algorithms use an objective function to 
evaluate the quality of alignment. The objective function 
summarizes the biological knowledge that is intended to be 
projected into the alignment. 

An alignment is considered to be correct if it reflects 
evolutionary history of the species of sequences being aligned. 
But, at the time of assessing the quality of alignment, such 
evolutionary information is not frequently available or even 
more not known [3]. 

One of existing measures for assessing the quality of 
alignment is obtained by computing similarity using 
substitutions matrices (for proteins). A substitution matrix 
assigns a cost for each possible substitution or conservation 
accordingly to the probability of occurrence computed from 
data analysis. In this approach insertions and deletions are 
weighted using affine gap penalties model. This model assigns 
weight cost for each gap opening and gap extension in order to 
favor alignments with smaller numbers of indels (each gap can 
be regarded as an insertion-deletion event). The main 
disadvantage of these substitution matrices is that they are 
intended to rate the similarity between two sequences at a time 
only. In order to extend them to multiple sequences, they are 
scaled by adding up each pairwise similarity to obtain the 
score of multiple sequence alignment [3, 4]. Another existing 
measure is to use weighted sum of pairs, is the objective 
function used by MSAs that associates a cost for each pair of 
aligned bases in each column of the alignment (substitution 
cost) and another similar cost for gaps (gap cost), sum of these 
costs yields a global cost of the alignment[8,7 and 11]. 
This paper presents a new approach for multiple sequence 
alignment using Rough Entropy as Global Criterion, REGC, to 
measure the quality of alignment. This approach doesn't use 
any traditional methods that compute similarity between all 
sequences using scoring functions, Rather than rough sets 
indcernibility relation used to collect DNA sequences in 
clusters .Each cluster contains set of sequences that are similar 
to each other. Gaps are inserted to each DNA sequence 
according to specified length then sequences are aligned 



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randomly by change offsets of gaps in each sequence. In each 
epoch, the random alignment is converted to suitable 
representation for rough set analysis, matrix form, where each 
sequence represents one row and each position of nucleotide 
inside each sequence represents one column. The value of 
each cell in the matrix can be a nucleotide base or gap 
according to random alignment. Indcernibility definition for 
missing values is used to compute the maximum clustering of 
sequences due to the presence of gaps. REGC used to measure 
the quality of alignment by maximize total number of 
sequences inside each cluster with respect to the total number 
of sequences. Each cluster has a name of one sequence and 
objects inside it are set of sequences that are indiscernible by 
knowledge P , set of attribute-value pairs which are 
corresponding to positions and values of bases inside each 
sequence. 

This paper introduces a global criterion to measure the quality 
of alignment by filling each cluster with the maximum number 
of sequences in alignment problem. The rough entropy value 
of knowledge P increases if the total number of sequences 
inside each clusters increases. When one cluster doesn't 
contain the maximum number of sequences, rough entropy 
approach finds similar missing sequences by dropping 
attributes from the knowledge P . If entropy value of 
knowledge P after dropping attributes increases, this means 
that the columns corresponding to those attributes must be 
realigned. The process repeat until the entropy value remains 
constant and hence the maximum numbers of sequences that 
are similar to each other are located inside each cluster. 
Paper is organized as follows: Section II gives an overview 
about preliminaries of DNA sequence alignment and rough 
sets. Section III presents the constructed hybrid model to align 
DNA sequences based on Rough sets and Rough Entropy. 
Section IV show simple experimented results of proposed 
model and section V conclude this paper. 

II. Preliminaries 

A. DNA Sequence Alignment 

DNA consists of two long strands of simple units called 
nucleotides, with backbones made of sugars and phosphate 
groups joined by ester bonds. These two strands run in 
opposite directions to each other and are therefore anti- 
parallel. Attached to each sugar is one of four types of letters 
called bases. The four bases found in DNA are adenine 
(abbreviated A), cytosine (C), guanine (G) and thymine (T). 
Each type of base on one strand forms a bond with just one 
type of base on the other strand. This is called complementary 
base pairing, with A bonding only to T, and C bonding only to 
G. The sequence of these four bases carries out the genetic 
information that is used in the development and functioning of 
all known living organisms, Thus DNA is the long-term 
storage of genetic information. DNA is copied and transmitted 
from parent to child, but from one generation to the next errors 
in copying can occur, with one nucleotide being replaced by 
another, or a new nucleotide being inserted, or an existing 
nucleotide being deleted [10,12 and 13]. Over many 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
generations, organisms with a common ancestor can end up 
with fairly different DNA sequences. 

To align two or more sequences, they are put together in a 
S - C matrix, where S is the number of sequences, and C is 
the maximum number of bases in a sequence (positions in the 
alignment); shorter sequences are filled at the end with gap 
("-") to fit the matrix perfectly. The goal of alignment process 
is to try to line up as many letters or portions of sequences that 
are the same, to minimize the number of substitutions, 
insertions, and deletions that would have had to happen if the 
sequences came from a common ancestor. In order to choose 
best alignment, the process of alignment has an objective to 
align homologous residues (having the same evolutionary 
origin) .The number of possible alignments of two sequences 
grows exponentially as the length of sequences increases 
[3]. So, with more sequences involved in the alignment 
process, the number of possible alignments grows faster and 
the problem of find optimal alignment becomes difficult to 
solve [3]. 



B. Rough Set Theory 

Rough set theory proposed by Pawlak [9] is an effective 
approach to imprecision, vagueness, and uncertainty. Rough 
set theory overlaps with many other theories such that fuzzy 
sets, evidence theory, and statistics. From a practical point of 
view, it is a good tool for data analysis. The main goal of the 
rough set analysis is to synthesize approximation of concepts 
from acquired data. The starting point of rough set theory is an 
observation that objects having the same description are 
indiscernible (similar) with respect to the available 
information. Determination of similar objects with respect to 
the defined attributes values is very hard and sensible when 
some attribute values are missing. This problem must be 
handled very carefully. The indiscernibility relation is a 
fundamental concept of rough set theory which used in the 
complete information systems. In order to process incomplete 
information systems, the indiscernibility relation needs to be 
extended to some equivalent relations. 

The starting point of rough set theory which is based on 
data analysis is a data set called information system ( IS ). IS 
is a data table, whose columns are labeled by attributes, rows 
are labeled by objects or cases, and the entire of the table are 

the attribute values. Formally, IS = ([/, AT J, where U and 
AT are nonempty finite sets called "the universe" and "the 
set of attributes," respectively. Every attribute a e AT , has a 
set of y of its values called the "domain of G ". If y 

contains missing values for at least one attribute, then S is 
called an incomplete information system, otherwise it is 
complete [5, 6, 8]. 
Any information table defines a function p that maps the 

direct product U x AT into the set of all values assigned to 
each attribute. The example of incomplete information system 
depicted in Table 1 where set of objects in the universe 
corresponding to set of DNA sequences and set of attributes 
corresponding to set of bases inside each sequence. The values 



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of attributes are corresponding to the values of bases inside 
each sequence such as the value of Sequence 1 at BaseO is 
defined by p(Seqi, Base ) = G 

TABLE 1 : Example of Incomplete Information System 



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Vol 8, No. 3, 2010 

Thus entry c ,, is the set of all attributes which discern objects 





Base 


Basei 


Base 2 


Seq! 


G 


A 


- 


Seq 2 


G 


T 


A 


Seq 3 


C 


C 


A 



The concept of the indiscernibility relation is an essential 
concept in rough set theory which is used to distinguish 
objects described by a set of attributes in complete information 
systems. Each subset A of AT defines an indiscernibility 
relation as follows: 

IND(A) = {(x,y)eUxU: p(x, a) = p(y, a) 

\/aeA,A^AT} U) 

The family of all equivalence classes of IND(A) is denoted 
by U/IND(A)or VIA [5, 6 and 8]. 
Obviously IND(A) is an equivalence relation and: 

IND(A) = fl IND(a) where aeA (2) 

A fundamental problem discussed in rough sets is whether 
the whole knowledge extracted from data sets is always 
necessary to classify objects in the universe; this problem 
arises in many practical applications and will be referred to as 
knowledge reduction. The two fundamental concepts used in 
knowledge reduction are the core and reduct. Intuitively, a 
reduct of knowledge is essential part, which suffices to define 
all basic classifications occurring in the considered 
knowledge, whereas core is in a certain sense the most 
important part. Let A set of attributes and let a e A , the 
attribute a is dispensable in A if: 

IND(A) = IND(A - {a}) (3) 

Otherwise a is indispensable attribute. The set ofy 
attributes B , where B a A is called reduct of A if: 

IND(B) = IND(A) (4) 

A may have many reducts. The set of all indispensable 
attributes in A will be called the core of A , and will be 
denoted as CORE ( A) : 

CORE (A) = RED (A) (5) 

Recently A.Skowron [8] has proposed to represent knowledge 
in a form of discernibility matrix .this representation has many 
advantages because it enables simple computation of the core 
and reduct of knowledge. 
Let K = (U,A) be a knowledge representation system with 

U = {j£i , x2'"">Xn) ky a discernibility matrix of 

K denoted by M (k) , which means nxn matrix defined by: 

(q) = i a eA:p(x 9 a) * p(y,a)}for ij = l,2....,n. (6) 

Identify applicable sponsor/s here, (sponsors) 



XI md Xj' 

The core can be defined now as the set of all single element 
entries of the discernibility matrix, i.e. 

CORE (A) = {ae A: c = (a) for some i,j}. (7) 

It can be easily seen that B d A is the reduct of A if B is 
the minimal subset of A such that B f] C ^ (j) for any 
nonempty entry c(c^(jj) in M (k) .In other words reduct is 
the minimal subset of attributes that discerns all objects 
discernible by the whole set of attributes. Let C,D<^A be 
two subsets of attributes, called condition and decision 
attributes respectively. KR-system with distinguished 
condition and decision attributes will be called a decision table 
and will be denoted T = (U,A,C,D) . Every xeU 

associate a function ^^ : A — » y , such 
that d x (a) = a(x) , for every ae C[jD ; the function 
j will be called a decision rule, and x will be referred to 
as a label of the decision rule j [5, 6, and 9]. 

III. Hybrid Model of Rough Sets and Rough Entropy 

The hybrid model discussed in this paper is considered to 
be a combination system that contains two methodologies 
which are rough sets and rough entropy. DNA sequences are 
converted to suitable representation for rough set analysis. The 
rough set indcernibility relation used to collect DNA sequences 
in clusters .Each cluster contains set of sequences that are 
similar to each other. Rough Entropy is used to measure the 
quality of alignment by maximize the total number of 
sequences inside each cluster with respect to the total number 
of sequences being aligned. 

A. Rough Sets analysis for Sequence Alignment 

The first part of the model consists of rough set analysis for 
DNA sequence alignment. Rough set approach used here is 
modified to deal with incomplete information system, 
where IIS = (U, AT) , where U and AT are nonempty 
finite sets called "the universe" and "the set of attributes," 
respectively. Every attribute a G AT , has a set of values 
called y and this set contains missing values for at least one 
attribute. In order to process incomplete information 
systems (IIS) , the indiscernibility relation has been extended 
to some equivalent relations such as similarity relation. 
Similarity relation SIM (P) denotes a binary relation between 

objects that are possibly indiscernible in terms of values of 
attributes and in the case of missing values the modified 
relation is defined by equation (8): 



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SIM(P) = {(x,y)eUxU, ae P,p(x,a) = p{y,a) 
or p(x,a) = * or p(y,a) = *} 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 



(8) 



Sp(x) = {yeU:(x,y)e SIM (PIP a AT} (9) 

SP(x) Denotes maximal set of objects which are possibly 

indiscernible by P with x [5, 6]. The indcernibility relation 
in rough sets, as depicted in equation (8), used to collect DNA 
sequences in clusters .Each cluster contains set of sequences 
that are similar to each other. Gaps are inserted to each DNA 
sequence according to specified length as depicted in Figure 1 
then the sequences are aligned randomly by change offsets of 
gaps in each sequence as depicted in Figure 2. 



GCATGCTA 

AGCTGC 

TAGCAA 

G C A CA T T - - 



Figure 1 : Gap insertion for Length=20 bases 



GC— A-TG— C T-A 

A— G CTG— C 

-T A GC— A A 

— G-C-A— CATT 



Figure 2: Random Alignment of DNA Sequences 

In each epoch, the random alignment is converted to suitable 
representation for rough set analysis, matrix form, where each 
sequence represents one row and each position of nucleotide 
inside each sequence represents one column. The value of 
each cell in the matrix can be a nucleotide base or gap 
according to random alignment as depicted in Table 2. 

TABLE 2: Snapshot of Rough Representation Table 

(a) 



u 


Po 


Pi 


P 2 


P 3 


P 4 


Si 


* 


* 


* 


G 


c 


s 2 


A 


* 


* 


G 


* 


s 3 


* 


T 


A 


G 


c 


s 4 


* 


* 


G 


* 


c 


(b) 


U 


p 5 


p 6 


P 7 


p 8 


p 9 


Si 


* 


* 


A 


* 


T 


s 2 


* 


* 


* 


C 


T 


s 3 


* 


* 


A 


* 


* 


s 4 


* 


A 


* 


* 


C 



Indcernibility definition of missing values is used here to 
compute the maximum clustering of sequences due to the 
presence of gaps as depicted in Figure 3. 




Figure 3: Set of Clusters corresponding to one Epoch 

B. Rough Entropy for assessing the Quality of Alignment 

The second part of the model uses REGC to measure the 
quality of DNA sequence alignment. The definition of rough 
entropy of knowledge in incomplete information system has 
been introduced as: 

Let IIS = (U,AT) , P^AT the rough entropy of 
knowledge P is defined by the following equation: 



|f/| \S! (r) 
E(P) = ~2^ 1tt1 ' log 



1 



u 



S P (x)\ 



(10) 



Where: 



u = \xvX 2 >X^ >J!k}' set of ob J' ects in 



the universe. 



C/ is the cardinality of set U . 
Log x = log 2 x. 

\SM)\ 



u 



represents the probability of tolerance 



class C p (j£.) within the universe U . 

1 

• | t denotes the probability of one of 

\S P (x)\ 

values in tolerance class S p^X^' 

The maximum value of rough entropy for knowledge P is 
computed by equation (11): 

E(P) = \u\logp\ (11) 

This value is achieved only by the equation (12): 

U/SIM(P) = \S P (x) = U\xeU} (12) 

The minimum of rough entropy for knowledge P is . This 
value is achieved only by the equation (13): 

U/SIM(P) = \S p (x) = {x}\xgu}[6]. (13) 

REGC is used to measure the quality of alignment by 
maximize the total number of sequences inside each cluster 
with respect to the total number of sequences. Each cluster has 
a name of one sequence such as depicted in Figure 3 and the 
objects inside it are set of sequences that are indiscernible by 
knowledge P , set of attribute-value pairs which are 
corresponding to positions and values of bases inside each 
sequence, such as depicted in Table 2. 

The aim of our approach is to make a global criterion to 
measure the quality of alignment by filling each cluster with 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 



the maximum number of sequences in alignment problem as 
depicted in Figure 4 where the total number of sequences 
being aligned is 4 sequences and the total number of clusters is 
4 clusters one for each sequence .REGC try to fill each cluster 
with maximum number of sequences similar to the sequence 
corresponding to that cluster. The alignment process ends 
when the total number of sequences located inside each cluster 
is equal to the total number of sequences being aligned. 



the Similar Sequences for Se0= 12 3 

the Similar Sequences for Sel= 12 3 

the Similar Sequences for Se2= 12 3 

the Similar Sequences for Se3= 12 3 



Figure 4: Maximum Clusters Set 

When the total number of sequences inside each cluster 
increases, the rough entropy value of knowledge P increases 
such as depicted in Figure 5 where the rough entropy value of 
P in Figure 5-b is higher than in 5- a because clusters in 5-b 
are filling with the maximum number of sequences that are 
similar to each other. 



the Similar Sequences for Se0= 12 

the Similar Sequences for Sel= 12 

the Similar Sequences for Se2= 12 

the Similar Sequences for Se3= 3 
E<A>=3. 5661656266226 



(a) 



[the Similar Sequences for Se0= 

the Similar Sequences for Sel= 

the Similar Sequences for Se2= 

the Similar Sequences for Se3= 
E<A>=8 



12 3 

12 3 

12 3 

12 3 



(b) 



Figure 5: Rough Entropy Value for Knowledge P 

When one cluster doesn't contain the maximum number of 
sequences, rough entropy approach finds similar missing 
sequences by dropping attributes from the knowledge P . If 
the entropy value of knowledge after dropping attributes 
increases, this means that the columns corresponding to those 
attributes must be realigned. The process repeat until the 
entropy value remains constant and hence the maximum 
number of sequences that are similar to each other are located 
inside each cluster. 



REGC Algorithm for MSA 

• Input: 

N : Total number of sequences to be aligned. 
M : Max Length. 
R : Randomization Times (optional). 

Si 9 S2 9 S3''"' Sn : Se( l uences bein S aligned. 

• Output: 

Alignment ofDNA Sequences 

1. [Start] insert gaps to all sequences until all have the 
same length M . 

2. [Alignment] 

o If ( R =1) create an alignment by randomly 
change the offset of gaps in each 
sequence. 

o If ( R =2,3,.. R ) create an alignment by 
randomly change the offset of gaps in 
columns indexed as realigned from 
attribute analysis step in previous epoch 

3. [Rough representation] convert the resulting random 
alignment to a suitable representation for rough set 
analysis , matrix form, where each sequence represent 
one row and each position of nucleotide inside each 
sequence represent one column. The value of each 
cell in the matrix can be a nucleotide base or gap 
according to random alignment. Rows corresponding 
to set of objects in the universe and columns are set 
of attributes, knowledge P 

4. [Rough Entropy] compute the entropy value of 
knowledge P that satisfies equation 10. 

5. [Attribute AnalysisJREGC analysis the entropy value 
by check each cluster if contains the total number of 
sequences being aligned and hence for any 

sequence x C (x) = U .When one cluster doesn't 

contain the maximum number of sequences, REGC 
finds similar missing sequences by dropping 

1,2, ,M — lattributes from the knowledge P. If 

the entropy value of knowledge after dropping 
attributes increases, this means that the columns 
corresponding to those attributes must be realigned. 

6. [Loop] go to step 2 until the maximum value of 

rough entropy of knowledge P is C/logC/ 

reached, this value is achieved only 

by U/SIM(P) = \S P W = U I x G U) , or until 

the maximum number of randomization times 
encountered. 



IV. Experimented Results 

All DNA fragments supplied to our model are obtained 
from Gene repository over the internet i.e. 
http://www.ncbi.nlm.nih.gov/Genbank/ [14] Genbank, is the 
NIH genetic sequence database, an annotated collection of all 



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publicly available DNA sequences. There are approximately 
106,533,156,756 bases in 108,431,692 sequence records in the 
traditional GenBank divisions and 148,165,117,763 bases in 
48,443,067 sequence records in the WGS division as of 
August 2009. GenBank is part of the International Nucleotide 
Sequence Database Collaboration, which comprises the DNA 
Databank of Japan (DDBJ), the European Molecular Biology 
Laboratory (EMBL), and GenBank at NCBI. These three 
organizations exchange data on a daily basis. 
Consider these four input fragments of DNA sequences which 
are given from Genbank. 

GCATGCTA 

AGCTGC 

TAGCAA 

GCACATT 
Where the other input parameters supplied to our program are 
Af=4,M=20and/?=100. 

1. [Start] insert gaps to all sequences until all have the 
same length 20. 

GCATGCTA 

AGCTGC 

TAGCAA 

GCACATT 

2. [Alignment] create an alignment by randomly change 
the offset of gaps in each sequence as depicted in 
Figure 6. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
(b) 



GC— A-TG— C T-A 












A— G CTG— C 












-TAGC— A A 












— G-C- A — C A T T 












the Similar Sequences 


for 


SeB = 





1 


2 


the Similar Sequences 


for 


Sel = 





1 


2 


the Similar Sequences 


for 


Se2 = 





1 


2 


the Similar Sequences 


for 


Se3 = 


3 







Figure 6: Random Alignment of DNA Sequences 

3. [Rough representation] convert the resulting random 
alignment to a suitable representation for rough set 
analysis , matrix form, where each sequence represent 
one row and each position of nucleotide inside each 
sequence represent one column. The value of each 
cell in the matrix can be a nucleotide base or gap 
according to random alignment. Rows corresponding 
to set of objects in the universe and columns are set 
of attributes, knowledge P , as described in Table 3: 

TABLE 3: Rough Representation of DNA Sequence Alignment 

(a) 



u 


Po 


Pi 


p 2 


P 3 


P 4 


p 5 


p 6 


P 7 


Ps 


P 9 


Si 


* 


* 


* 


G 


C 


* 


* 


A 


* 


T 


s 2 


A 


* 


* 


G 


* 


* 


* 


* 


C 


T 


s 3 


* 


T 


A 


G 


c 


* 


* 


A 


* 


* 


s 4 


* 


* 


G 


* 


c 


* 


A 


* 


* 


C 



u 


Pio 


Pn 


Pl2 


Pl3 


Pl4 


Pis 


Pl6 


Pl7 


Pl8 


Pl9 


s, 


G 


* 


* 


C 


* 


* 


* 


T 


* 


A 


s 2 


G 


* 


* 


C 


* 


* 


* 


* 


* 


* 


s 3 


* 


A 


* 


* 


* 


* 


* 


* 


* 


* 


s 4 


A 


T 


T 


* 


* 


* 


* 


* 


* 


* 



For 


Se0 = 


O 


1 


2 


for 


Sel = 





1 


2 


for 


Se2 = 





1 


2 


for 


Se3 = 


3 







4. [Rough Entropy] compute the entropy value of 
knowledge P that satisfies equation 10 as depicted 
in Figure 7. 



— G-C-A— CATT 

GC— A-TG— C T-A 

I— G CTG— C 

T A GC— A A 

—G-C-A— CATT 

;he Similar Sequences 
;he Similar Sequences 
;he Similar Sequences 
;he Similar Sequences 
i<A>=3. 5661656266226 



Figure 7: Rough Entropy Value of Knowledge P 

5. [Attribute Analysis] when one cluster doesn't contain 
the maximum number of sequences, rough entropy 
approach finds similar missing sequences by 
dropping one, two... M— 1 attributes from 
knowledge P . Dropping attributes leads to increase 
the number of sequences inside each cluster and 
hence the value of entropy increases. The increasing 
value of entropy after dropping the attributes 
indicates that some columns in the alignment are 

needed to realign and ^ P (^)^U for any 
sequence X .When dropping one attribute from 
knowledge P as depicted in Figure 8, the value of 
entropy remains constant. Every cluster doesn't 
contain all sequences in the alignment and the 

equation § (x) ^ U satisfied for any sequence X . 

When dropping the 

attributes P 2 , P 9 , P lQ and p n , the value of 

entropy increased to 8 as depicted in Figure 9 
.Increasing the value of entropy indicates that all 
columns corresponding to those attribute must be 
realigned to satisfy this value. 



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GC — A -T G — C T -A 

ft — G CT G — C 

-T A GC — A A 

— G-C- A — C A T T 

the Similar Sequences for 

the Similar Sequences for 

the Similar Sequences for 

the Similar Sequences for 
E<A> =3.5661656266226 

the Similar Sequences for 

the Similar Sequences for 

the Similar Sequences for 

the Similar Sequences for 
E<A-P0>=3 .5661656266226 

the Similar Sequences for 

the Similar Sequences for 

the Similar Sequences for 

the Similar Sequences for 
E<A-P1>=3 .5661656266226 

the Similar Sequences for 

the Similar Sequences for 

the Similar Sequences for 

the Similar Sequences for 
E<A-P2>=3 .5661656266226 



Figure 8: Relation between Dropping Attributes of Knowledge P and Rough 

Entropy 

6. [Loop] go to step 2 until the maximum value of 

rough entropy of knowledge P is |[/| log t/ reached 

as depicted in Figure 9 , this value is achieved only 

by the U/SIM(P) = \S P (x) = U I x e u\ , or 

until the maximum number of randomization times 
encountered. 



SeB = 





1 


2 


Sel = 





1 


2 


Se2 = 





1 


2 


Se3 = 


3 






SeS = 





1 


2 


Sel = 





1 


2 


Se2 = 





1 


2 


Se3 = 


3 






Se0 = 





1 


2 


Sel = 





1 


2 


Se2 = 





1 


2 


Se3 = 


3 






SeB = 





1 


2 


Sel = 





1 


2 


Se2 = 





1 


2 


Se3 = 


3 







— GCATGCTA- 

















-AGC-TGC 

















TAGCA A- 

















— GCA— C-ATT — 














the Similar 


Sequences 


for 


SeB = 







2 


3 


the Similar 


Sequences 


for 


Sel = 







2 


3 


the Similar 


Sequences 


for 


Se2 = 







2 


3 


the Similar 


Sequences 


for 


Se3 = 







2 


3 


E<A>=8 
















the Similar 


Sequences 


for 


SeB = 







2 


3 


the Similar 


Sequences 


for 


Sel = 







2 


3 


the Similar 


Sequences 


for 


Se2 = 







2 


3 


the Similar 


Sequences 


for 


Se3 = 







2 


3 


E<A-P0>=8 
















the Similar 


Sequences 


for 


SeB = 







2 


3 


the Similar 


Sequences 


for 


Sel = 







2 


3 


the Similar 


Sequences 


for 


Se2 = 







2 


3 


the Similar 


Sequences 


for 


Se3 = 







2 


3 


E<A-P1>=8 
















the Similar 


Sequences 


for 


Se0 = 







2 


3 


the Similar 


Sequences 


for 


Sel = 







2 


3 


the Similar 


Sequences 


for 


Se2 = 







2 


3 


the Similar 


Sequences 


for 


Se3 = 







2 


3 


E<A-P2>=8 
















the Similar 


Sequences 


for 


Se0 = 







2 


3 


the Similar 


Sequences 


for 


Sel = 







2 


3 


the Similar 


Sequences 


for 


Se2 = 







2 


3 


the Similar 


Sequences 


for 


Se3 = 







2 


3 


E<A-P3>=8 
















the Similar 


Sequences 


for 


Se0 = 







2 


3 


the Similar 


Sequences 


for 


Sel = 







2 


3 


the Similar 


Sequences 


for 


Se2 = 







2 


3 


the Similar 


Sequences 


for 


Se3 = 







2 


3 


E<A-P4>=8 
















the Similar 


Sequences 


for 


Se0 = 







2 


3 


the Similar 


Sequences 


for 


Sel = 







2 


3 


the Similar 


Sequences 


for 


Se2 = 







2 


3 


the Similar 


Sequences 


for 


Se3 = 







2 


3 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
V. Conclusions 

This paper presents a new approach for multiple sequence 
alignment using REGC to measure the quality of the 
alignment. This approach doesn't use any traditional methods 
that compute similarity between all sequences using scoring 
functions, Rather than rough set indcernibility relation used to 
collect DNA sequences in clusters where each cluster contains 
set of sequences that are similar to each other. Gaps are 
inserted to each DNA sequence according to specified length 
then sequences are aligned randomly by change offsets of gaps 
in each sequence. REGC is used to measure the quality of 
alignment by maximize the total number of sequences inside 
each cluster with respect to the total number of sequences 
being aligned. Each cluster has a name of one sequence and 
the objects inside it are set of sequences that are indiscernible 
by knowledge P , set of attribute-value pairs which are 
corresponding to positions and values of bases inside each 
sequence. REGC used to measure the quality of alignment by 
filling each cluster corresponding to one sequence with the 
maximum number of sequences in the alignment problem .The 
rough entropy value of knowledge P increases when the total 
number of sequences inside each cluster increases. If one 
cluster doesn't contain the maximum number of sequences, 
rough entropy approach finds similar missing sequences by 
dropping attributes from knowledge P . If the entropy value 
of knowledge after dropping attributes increases, this means 
that the columns corresponding to those attributes must be 
realigned. The process repeat until the maximum value of 

rough entropy of knowledge P is \U \log\U\ reached; this 



value is achieved 

by U/SIM(P) = \S P (x) = U I x g U) 
sequenced . 



for 



only 
any 



Figure 9: Relation between Dropping Attributes of Knowledge P and Rough 

Entropy 



Acknowledgment 

I would like to thank my father Dr.El-Sayed El-Metwally 
and my mother Dr.Hemmat El-Shik for their moral support I 
required in my life at all. 

I am heartily thankful to my supervisor, Dr. ElSayed 
Radwan, whose encouragement, supervision and support from 
the preliminary to the concluding level enabled me to develop 
an understanding of the subject. 

Lastly, I offer my regards and blessings to all of those who 
supported me in any respect during the completion of the 
paper. 



References 

[1] Arthur M.Lesk, Introduction to Bioinformatics, University 
of Cambridge, USA, Oxford University Press,2002 

[2] Bryan Bergeron, Bioinformatics Computing, University of 
British Columbia, USA, Prentice Hall,2002 

[3] Edgar D. Arenas-Diaz, Helga Ochoterena, and Katya 
Rodriguez-Vazquez, Multiple Sequence Alignment Using 
a Genetic Algorithm and GLOCSA, Universidad Nacional 
Aut'onoma de Mexico, Genetic and Evolutionary 
Computation Conference, Proceedings of the 2008 GECCO 



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conference companion on Genetic and evolutionary 
computation, pp. 1795-1798, USA, ACM, 2008. 

[4] Edward Keedwell and Ajit Narayanan, Intelligent 
Bioinformatics, School of Engineering and Computer 
Sciences, University of Exeter, UK, Wiley, 2005. 

[5] E. A. Rady, M. M. E. Abd El-Monsef, and W. A. Abd El- 
Latif, A Modified Rough Set Approach to Incomplete 
Information Systems, Journal of Applied Mathematics and 
Decision Sciences, Article ID 58248, Egypt, Hindawi 
Publishing Corporation, , Volume 2007 

[6] JIYE LIAN and ZONGBEN XU, The Algorithm on 
Knowledge Reduction in Incomplete Information 
Systems, Shanxi University, Taiyuan, International journal of 
uncertainty, Fuzziness and Knowledge based systems, 
Volume 2002. 

[7] Koji Tajima, Multiple Sequence Alignment Using Parallel 
Genetic Algorithms, Institute for social information Science 
,FUJITSU LABORATORIES LTD, Japan ,Genome 
Informatics Workshop IV, Volume 4, 1993 

[8] Narayanan, E.C. Keedwell and B. Olsson, Artificial 
Intelligence Techniques for Bioinformatics, School of 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
Engineering and Computer Sciences, University of Exeter, 
Exeter EX4 4QF, UK, Journal of Applied Bioinformatics , 
2005. 

ZDZISLAW PAWLAK, Rough Sets Theoretical Aspects of 
Reasoning about Data, Institute of Computer Science, 
Warsaw University of Technology, Australia, Kluwer 
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http://en.wikipedia.org/wiki/Multiple sequence alignment 
10-7-2009 

http://oreillv.com/news/bioinformatics 040 1 .html 2-3-2008 
http://www.ebi.ac.uk/2can/tutorials/transcription.html 
4-4-2008 

http://en.wikipedia.org/wiki/Genetic code 6-5-2008 
http://www.ncbi.nlm.nih.gov/Genbank/ 5-1-2010 



[9] 



[10] 

[11] 
[12] 

[13] 
[14] 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 



Weighted Attribute Fusion Model for Face 

Recognition 



S.Sakthivel 

Assistant Professor, Department of Information 

Technology 

Sona college of Technology, Salem, India 

sakthits @ rediffmail.com 



Dr.R.Lakshmipathi 

Professor, Department of Electrical and Electronic 

Engineering 

St.Peter's Engineering College, Chennai, India 

drrlakshmipathi@yahoo.com 



Abstract — Recognizing a face based on its attributes is an easy 
task for a human to perform as it is a cognitive process. In recent 
years, Face Recognition is achieved with different kinds of facial 
features which were used separately or in a combined manner. 
Currently, Feature fusion methods and parallel methods are the 
facial features used and performed by integrating multiple 
feature sets at different levels. However, this integration and the 
combinational methods do not guarantee better result. Hence to 
achieve better results, the feature fusion model with multiple 
weighted facial attribute set is selected. For this feature model, 
face images from predefined data set has been taken from 
Olivetti Research Laboratory (ORL) and applied on different 
methods like Principal Component Analysis (PCA) based Eigen 
feature extraction technique, Discrete Cosine Transformation 
(DCT) based feature extraction technique, Histogram Based 
Feature Extraction technique and Simple Intensity based 
features. The extracted feature set obtained from these methods 
were compared and tested for accuracy. In this work we have 
developed a model which will use the above set of feature 
extraction techniques with different levels of weights to attain 
better accuracy. The results show that the selection of optimum 
weight for a particular feature will lead to improvement in 
recognition rate. 

Keywords- Face Recognition, Feature Fusion Method, Parallel 
Method, PCA, DCT, Histogram Matching 



I. 



Introduction 



Face recognition is an important part of today's emerging 
biometrics and video surveillance markets. Face Recognition 
can benefit the areas of: Law Enforcement, Airport Security, 
Access Control, Driver's Licenses & Passports, Homeland 
Defense, Customs & Immigration and Scene Analysis. Face 
recognition has been a research area for almost 30 years, with 
significantly increased research activity since 1990[16] [15]. 
This has resulted in the development of successful algorithms 
and the introduction of commercial products. But, the 
researches and achievements on face recognition are still in its 
initial stages of development. Although face recognition is still 
in the research and development phase, several commercial 
systems are currently available and research organizations 
are working on the development of more accurate and 
reliable systems. Using the present technology, it is 

impossible to completely model human recognition system and 
reach its performance and accuracy. However, the human 



brain has its shortcomings in some aspects. The benefits of a 
computer system would be its capacity to handle large amount 
of data and ability to do a job in a predefined repeated manner. 
The observations and findings about human face recognition 
system will be a good starting point for automatic face 
attribute. 

A. Early Works 

Face recognition has gained much attention in the last two 
decades due to increasing demand in security and law 
enforcement applications. Face recognition methods can be 
divided into two major categories, appearance-based method 
and feature-based method. Appearance-based method is more 
popular and achieved great success [3]. 

Appearance-based method uses the holistic features of a 2- 
D image [3]. Generally face images are captured in very high 
dimensionality, normally which is more than 1000 pixels. It is 
very difficult to perform face recognition based on original face 
image without reducing the dimensionality by extracting the 
important features. Kirby and Sirovich first used principal 
component analysis (PCA) to extract the features from face 
image and used them to represent human face image [16]. PCA 
seeks for a set of projection vectors which project the image 
data into a subspace based on the variation in energy. Turk and 
Pentland introduced the well-known Eigenface method [15]. 
Eigenface method incorporates PCA and showed promising 
results. Another well-known method is Fisher face. Fisher face 
incorporates linear discriminant analysis (LDA) to extract the 
most discriminant features and to reduce the 
dimensionality [3]. when it comes to solving problems of 
pattern classification, LDA based algorithms outperform PCA 
based ones, since the former optimizes the low dimensional 
representation of the objects with focus on the most 
discriminant feature extraction while the latter achieves simply 
object reconstruction [9][10][1 1]. Recently there has been a lot 
of interest in geometrically motivated approaches to data 
analysis in high dimensional spaces. This case is concerned 
with data drawn from sampling a probability distribution that 
has support on or near a sub manifold of Euclidean space [5]. 

Let us consider a collection of data points of n-dimensional 
real vectors drawn from an unknown probability distribution. 
In increasingly many cases of interest in machine learning and 
data mining, one is confronted with the situation which is very 



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large. However, there might be reason to suspect that the 
"intrinsic dimensionality" of the data is much lower. This leads 
one to consider methods of dimensionality reduction [1][2][8] 
that allow one to represent the data in a lower dimensional 
space. A great number of dimensionality reduction techniques 
exist in the literature. 

In practical situations, where data is prohibitively large, one 
is often forced to use linear (or even sub linear) techniques. 
Consequently, projective maps have been the subject of 
considerable investigation. Three classical yet popular forms of 
linear techniques are the methods of PC A [6] [14] [1][2], 
multidimensional scaling (MDS) [6] [14], and LDA [14] [11]. 
Each of these is an eigenvector method designed to model 
linear variability's in high-dimensional data. More recently, 
frequency domain analysis methods [3] such as discrete 
Fourier transform (DFT), discrete wavelet transform (DWT) 
and discrete cosine transform (DCT) have been widely adopted 
in face recognition. Frequency domain analysis methods 
transform the image signals from spatial domain to 
frequency domain and analyse the features in frequency 
domain. Only limited low-frequency components which 
contain high energy are selected to represent the image. 
Unlike PCA, frequency domain analysis methods are data 
independent [3]. They analyse image independently and do not 
require training images. Furthermore, fast algorithms are 
available for the ease of implementation and have high 
computation efficiency. 

In [3] new parallel models for face recognition were 
presented. Feature fusion is one of the easy and effective ways 
to improve the performance. Feature fusion method is 
performed by integrating multiple feature sets at different 
levels. However, feature fusion method does not guarantee 
better result [3]. One major issue is feature selection. Feature 
selection plays a very important role to avoid overlapping 
features and information redundancy. New parallel model for 
face recognition utilizes information from frequency and 
spatial domains, addresses both features and processes in 
parallel way. It is well- known that image can be analysed 
in spatial and frequency domains. Both domains describe 
the image in very different ways. The frequency domain 
features [3] are extracted using techniques like DCT, DFT and 
DWT methods respectively. By utilizing these two or more 
very different features, a better performance is guaranteed. 

Feature fusion method suffers from the problem of high 
dimensionality because of the combined features. It may also 
contain redundant and noisy data. PCA is applied on the 
features from frequency and spatial domains to reduce the 
dimensionality and extract the most discriminant information 
[3]. It is surprising that until recent study demonstrated 
that colour information makes contribution and enhances 
robustness in face recognition [4]. 

II. MATERIALS AND METHODS 

In statistics, dimension reduction is the process of reducing 
the number of random variables under consideration, and can 
be divided into feature selection and feature extraction. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
A. Extracting Eigen Features El 

In previous work [1] five algorithms are evaluated, namely 
PCA, Kernel PCA, LDA [9] [10], Locality Preserving 
Projections (LPP) [1] [8] and Neighbourhood Preserving 
Embedding (NPE) [1][2] [5] for dimensionality reduction and 
feature extraction and found that Kernel PCA was the best 
performer. This work uses PCA based algorithm to show some 
improvements in its performance due to the use of Multiple 
Weighted Facial Attribute Sets. The basic idea of PCA [5] is to 
project the data along the directions of maximal variances so 
that the reconstruction error can be minimized. Given a set of 
data points xl... xn, let a be the transformation vector and yi = 



a x t . The objective function of PCA is as follows: 



a = argmaxY (y. -y ) 2 = argmax a T C a 



-d) 



*=i 



In equation y = — 2, yi and C is the data covariance the 
n 
eigen. The basic functions of PCA are the eigenvectors of the 
data covariance matrix corresponding to the largest 
eigen values. While PCA seeks direction that are efficient for 
representation. 

B. Extracting DCT Features E2 

The DCT [2] can be used to Create DCT feature Set of the 
Face. The Discrete Cosine Transform is a real domain 
transform which represents the entire image as coefficients of 
different frequencies of cosines (which are the basis vectors for 
this transform). The DCT of the image is calculated by taking 
8x8 blocks of the image in Figure 1, which is then transformed 
individually. The 2D DCT of an image gives the result matrix 
such that top left corner represents lowest frequency coefficient 
while the bottom right corner is the highest frequency. 



I - 
















r 


l_ 


















1= 
















1 


w\ 






















































H 





















Figure 1 The frequency domain representation of an image 
The 1-D discrete cosine transform (DCT) is defined as 
(2x + t)U7T 

J w * ^ 

x=0 



C(u) = a(u)^f(x) • cos 



IN 



(2) 



Similarly, the inverse DCT is defined as 

N - 1 r (2x + l)ux~ 

w=0 



f(x) = ^a(u)c(u)-cos 



IN 



(3) 



for x= 0, 1 ,2, . . . ,N 1 . In both equations (2) and (3) 
(u) is defined as 



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a(u) = 




for u = l,2,...,N-l 



The corresponding 2-D DCT, and the inverse DCT are 
defined as 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
major learning paradigms, each corresponding to a particular 
abstract learning task. These are supervised learning, 
unsupervised learning and reinforcement learning. Usually any 
given type of network architecture can be employed in any of 
those tasks. 



■(4) 



NANA 



^vj^^yyK^y)^ ^ 



j^OrO 



(2xA-i)u7T 



2N 



co& 



"M 



\vn 



2/V 



■(5) 



for u,v =0,1,2, . . . JSf 1 and (u) and (v) are defined in (4). 
The inverse transform is defined as 



f(x,y) = ^^a(w)a(v)c(w,v)-cos 



(2x + l)u7T 



2N 



\2y + \)v7i 



2N 



(6) 

The advantage of DCT is that it can be expressed without 
complex numbers. The DCT transform Equation (5) can be 
expressed as separable, (like 2-D Fourier transform), i.e. it can 
be obtained by two subsequent 1-D DCT in the same way as 
Fourier transform. Equation (6) shows the Inverse 
transformation. 

C. The Histogram Feature Vector F3 

The distribution of gray levels occurring in an image is 
called gray level histogram. It is a graph showing the frequency 
of occurrence of each gray level in the image versus the gray 
level itself. The plot of this function provides a global 
description of the appearance of the image. The histogram of a 
digital image with gray levels in the range [0, L-l] is a discrete 
function . 



n k /n 



(7) 



P (r k ) = 

Where, 

r k -> Kth gray level 

n k -> No of pixels in the image with that gray level. 

n -> total number of pixels in the image. 

K =0,1, 2... L-l. 

L = 256. (For 256 level gray images) 

In Equation 7 P(r k ) gives an estimate of the probability of 
occurrence of gray level r k If we use L value of small size, 
then n k will contain a range of nearest values in L number f 
bins. So for constructing Histogram Based Feature, the set n k 
and the mid values of the bin n k were combined. 

D. The Intensity Based Feature F4 

Intensity Feature set can be formed by using values like 
mean Median Mode of the 256 gray level images. We can use 
one or many of this values to represent the average intensity of 
the face image. 

E. Neural Networks and Learning Paradigms 

In principle, the popular neural network can be trained to 
recognize face images directly. However, a simple network can 
be very complex and difficult to train [12] [17]. There are three 



F. Learning Algorithms 

Training a neural network model essentially means 
selecting one model from the set of allowed models (or, in a 
Bayesian framework, determining a distribution over the set of 
allowed models) that minimizes the cost criterion. There are 
numerous algorithms available for training neural network 
models; most of them can be viewed as a straightforward 
application of optimization theory and statistical estimation. 
Most of the algorithms used in training artificial neural 
networks are employing some form of gradient descent. This is 
done by simply taking the derivative of the cost function with 
respect to the network parameters and then changing those 
parameters in a gradient-related direction. Evolutionary 
methods simulated annealing, and Expectation-maximization 
and non-parametric methods are among other commonly used 
methods for training neural networks. 

G. Support vector machines (SVMs) 

Support vector machines are a set of related supervised 
learning methods used for classification and regression. 
Viewing input data as two sets of vectors in an n-dimensional 
space, an SVM will construct a separating hyper plane in that 
space, one which maximizes the margin between the two data 
sets[7][13]. To calculate the margin, two parallel hyper planes 
are constructed, one on each side of the separating hyper plane, 
which is "pushed up against" the two data sets. Intuitively, a 
good separation is achieved by the hyper plane that has the 
largest distance to the neighbouring data points of both classes, 
since in general the larger the margin the better the 
generalization error of the classifier. For the linearly separable 
case, a hyper-plane separating the binary decision classes in the 
three-attribute case can be represented as the following 
equation: 

(8) 



In Equation (8), y is the outcome x,, are the attribute values, 
and there are four weights w } to be learned by the learning 
algorithm. In the above equation, the weights Wj are parameters 
that determine the hyper-plane. The maximum margin hyper- 
plane can be represented as the following equation in terms of 
the support vectors: 



y 



■■b + ^^yMxityx) 



(9) 



In Equation (9) the function K(x(t).x) is defined as the 
kernel function. There are different kernels for generating the 
inner products to construct machines with different types of 
nonlinear decision surfaces in the input space. 

SVM is selected as the classifying function. One distinctive 
advantage this type of classifier has over traditional neural 
networks is that SVMs can achieve better generalization 
performance. Support vector machine is a pattern classification 
algorithm developed by Vapnik [13]. It is a binary 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 



classification method that finds the optimal linear decision 
surface based on the concept of structural risk minimization. As 
shown by Vapnik, this maximal margin decision boundary can 
achieve optimal worst-case generalization performance. Note 
that SVMs are originally designed to solve problems where 
data can be separated by a linear decision boundary 

III. TheModel of proposed system 

Given a set of feature vectors belonging to n classes, a 
Support Vector Machine (SVM) finds the hyper plane that 
separates the largest possible fraction of features of the same 
classes on the corresponding space, while maximizing the 
distance from either class to the hyper plane. Generally a 
suitable transformation is first used to extract features of face 
images and then discrimination functions between each class of 
images are learned by SVMs. Figure 2 shows the feature set 
creation for face images from ORL database whereas Figure 3 
shows the architecture for training and testing the SVM with 
weighted attribute set. 



Load n Training Face Images from ORL Face 


Database 




w 


Resize the Ima 


ges in to 48x48 


Reshape the Images in to lx 2304 and Prepare 


n x 2304 Matrix 'M' 


i 




i i i 


Find the 




Find 


Find 


Find the 


Eigen 




DCTof 


Histogra 


Average 


Vector 




all the 


m of all 


Intensity 


Matrix 




Row 


the Row 


of Each 


'V and 




elements 


elements 


Row of 


Project 




of 


of 


'M' and 


the 




Matrix 


Matrix 


form a 


Matrix 




'M' to 


'M' to 


Intensity 


MonV 




form 


form A 


Feature 


to find 




DCT 


Histogra 


Matrix 


the 




feature 


m 


F4 


Eigen 




Matrix 


feature 




Feature 




'D' and 


Matrix 




Matrix 




Find 


'F3' 




Fl 




PCAto 

form a 
Feature 
Matrix 
F2 






i 


r 




i 


r ^ 


r i 


r 


Feature 




Feature 


Feature 


Feature 


SetFl 




SetF2 


SetF3 


SetF4 



Feature 
SetFl 



Feature 
SetF2 



Multiply 
with 
Weight 
Wl 



Feature 
SetF3 



Multiply 

with 

Weight 

W2 



Feature 
SetF4 



Multiply 
with 
Weight 
W3 



Multiply 
with 

Weight 
W4 



F1*W1 



F2*W2 



IZ 



F3*W3 



±2. 



F4*W4 



1Z 



The Input Layer of SVM Neural Network 



The Final Trained SVM Neural Network 



Figure 2: The Feature Set Creation 



Figure 3: Architecture for Training and Testing the SVM with Weighted 
Attribute Set 



A. Steps Involved in Training 

1) Load a set of 'n' ORL Face Images For Training 

2) Resize the Images in to 48x48 pixel size to reduce the 
memory requirements of the overall application 

3) Reshape the Images in to lx 2304 and prepare an n x 2304 
Feature Matrix representing the training data set. 

4) Apply a Feature Extraction, DCT, Histogram and 
Dimensionality Reduction technique and find the Four of 
above said Features sets Fl, F2, F3 and F4. 

5) Multiply the Weights Wl, W2, W3 and W4 with Fl, F2, 
F3 and F4. 

6) Create an SVM network with "fl+f2+f3+f4" inputs where 
fl, f2, f3 and f4 are the corresponding feature lengths. 

7) Train a SVM using the Weighted Feature Set. 

B. Steps Involved in Testing 

1) The first three steps of the above procedures will be 
repeated with test image set of ORL database to obtain a 
Feature Matrix representing the testing data set. 

2) Project the matrix using Previous Eigen Vector Matrix and 
find the input Eigen Feature iFl. 

3) Similarly find other input feature sets iF2, iF3 and iF4 of 
the input Image. 

4) Classify the feature set [iFl iF2 iF3 iF4] with previously 
trained SVM network. 

5) Calculate Accuracy of Classification 



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IV . Implementation results and analysis 



The performance of proposed face recognition model was 
tested with the standard set of images called "ORL Face 
Database". The ORL Database of Faces contains a set of face 
images used in the context of a face recognition project carried 
out in collaboration with the Speech, Vision and Robotics 
Group of the Cambridge University Engineering Department. 
There are ten different images, each of 40 distinct subjects. For 
some subjects, the images were taken at different times, 
varying the lighting, facial expressions (open / closed eyes, 
smiling / not smiling) and facial details (glasses / no glasses). 
All the images were taken against a dark homogeneous 
background with the subjects in an upright, frontal position 
(with tolerance for some side movement). 

Set of images from ORL databases were used for Training 
and Testing. The accuracy of recognition with multiple 
weighted attribute sets as well as a single attribute sets has been 
evaluated. The following table shows the overall results of 
these two types of techniques with different number of input 
face images. 

Table 1 Accuracy of recognition with single attribute set 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
well as multiple attribute sets with same priority or weight will 
lead to poor recognition results. For example, all the results of 
Table 1 which used a single attribute at a time for recognition, 
is in some what poor than Table 2 which is using combined 
multi attributes. Further, if we note the fourth row (40 images) 
corresponding to the weights (Wl=l, W2=l, W3=l, W4=l) 
which is using all the attributes with same weight, the result is 
poor while comparing it with column 2 (Wl=0.5, W2=l, 
W3=0, W4=0). The following Graphs show the performance of 
the two different approaches. In Figure 4 Line charts shows the 
Performance with single attribute set, but in Figure 5 Line 
charts shows the performance with multiple weighted attribute 
sets. 



No. of 

Faces used 

for 

Training 

and 

Testing 


Accuracy of Recognition with different 
Weight Sets (%) 


Wl=l 
W2=0 
W3=0 
W4=0 


W1=0 

W2=l 
W3=0 
W4=0 


W1=0 

W2=0 
W3=l 
W4=0 


W1=0 

W2=0 
W3=0 
W4=l 


10 


90.00 


40.00 


80.00 


90.00 


20 


80.00 


25.00 


60.00 


65.00 


30 


83.33 


16.67 


56.67 


76.67 


40 


80.00 


12.50 


52.50 


70.00 


Average 


83.33 


23.54 


62.29 


75.42 



Table 2 Accuracy of recognition with multiple attribute set 



No. of 

Faces 

used for 

Training 

and 

Testing 


Accuracy of Recognition with different 
Weight Sets (%) 


Wl=l 
W2=l 
W3=0 
W4=0 


Wl=5 
W2=l 
W3=0 
W4=0 


Wl=5 
W2=l 
W3=0 
W4=l 


Wl=l 
W2=l 
W3=l 
W4=l 


Wl=. 12 
W2=0 
W3=l 
W4=0 


10 


90.00 


100 


100 


100 


100 


20 


80.00 


85.00 


85.00 


85.00 


90.00 


30 


86.67 


86.67 


86.67 


83.33 


86.67 


40 


82.50 


85.00 


82.50 


82.50 


82.50 


Average 


84.79 


89.17 


88.54 


87.71 


89.79 



As shown in the above table and the following graphs, the 
performance of recognition while using Single Attribute Set as 



100 
90 

: 8° 
: 70 
! 60 



O 50 



40 
30 
20 
10 



Performance Wth Different Attributes 



-♦- Bgen Features 

-■- Intensity 
Gtay histogram 
DCTFeatLiies 



10 20 30 40 

Nurrber of Face Images 



Figure 4 Performance with single attribute 



Perfonranoe Wth afferent Attributes and Waghts 




20 30 

Number of Faoe Images 



-wi=i,v\e=i 5 v\iB=o 5 wida -^wida5,v\2=i 5 v\G^wida 

WI=0AV\fi=1,V\G=0 5 WI=1. WI=1,V\fe1,V\fc1,Wfe1. 
-WI^1%V\E=0,V\lB=1,Wl=0. 



Figure 5 Performance with multiple weighted attribute sets 



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The Following Two Charts shows the average performance 
of the two approaches. In Figure 6, column charts shows the 
average performance with single attribute set. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
V. CONCLUSION 



on nn -, 






yu.uu 
an on - 




83.33 












ou.uu 
7n n n - 






/5AZ 










/u.uu 
en nn - 






62.29 








DU.UU 

£ n nn - 








ou.uu 

/in nn - 








4U.UU 

o n nn - 








ou.uu 
o n nn - 




23.54 








1 n nn - 








I u.uu 
n nn - 






U.UU i 








■ W1=1,W2=0,W3 = 0,W4=0. ■W1=0.5,W2 = 1,W3=0,W4=0. 
□ W1=0,W2=0,W3 = 1,W4=0. □ W1=0,W2 = 0,W3 = 0,W4 = 1. 













Figure 6 average performance with single attribute set 

In Figure 7, column charts shows the average performance 
with multiple attribute sets. 



91.00 




□ W1 =1 ,W2=1 ,W3=0,W4=0. 

□ W1 =0.5,W2=1 ,W3=0,W4=1 . 
■ W1 =.1 2,W2=0,W3=1 ,W4=0. 



■ W1 =0.5,W2=1 ,W3=0,W4=0. 
□ W1=1,W2=1,W3=1,W4=1. 



Figure 7 Average performances with multiple attribute set 



Complicated Face recognition techniques constantly facing 
very challenging and difficult problems in several research 
works. In spite of the great work done in the last 30 years, it is 
sure that the face recognition research community will have to 
work for at least the next few decades to completely solve the 
problem. Strong and coordinated efforts between the computer 
visions signal processing and psychophysics and neurosciences 
community is needed. The proposed Multiple Weighted 
Feature Attribute Sets based training provided significant 
improvement in terms of performance accuracy of the face 
recognition system. With ORL data set, a significant 5% 
performance improvement was observed during various tests. 
In this work, we have selected PCA as the main feature 
extraction technique. The weights of the used feature sets were 
decided based on trial and error method. This will be applicable 
for systems with predefined data sets. In future works, one may 
explore different techniques like Kernel PCA, LDA for better 
performance. So, future works may address methods for 
automatic estimation of the weights of the feature sets with 
respect to the application. 

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Vol 8, No. 3, 2010 



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[16] Kirby, M. & Sirovich, L. (1990) Application of the Karhunen-Loeve 
procedure of the characteristic of human faces, IEEE Trans. Pattern 
Anal. Machine Intell, vol.12, pp 103-108, Jan, 1990. DOI 
10.1109/34.41390 

[17] Shang-Hung Lin, "An Introduction to Face Recognition Technology", 
Information science special issue on multimedia informing Technologies 
- part2 volume 3 No 1, 2000. http://inform.nu/Articles/Vol3/v3nlp01- 
07.pdf 



AUTHORS PROFILE 

1 S. Sakthivel received his M .E Computer science from Sona College of 
Technology, Affiliated to Anna University, Chennai, India in the year 
2004. Currently, pursuing his Ph.D., in Anna University, 
Chennai,Tamilnadu.He has a work experience of 10 years. At present 
working as a Assistant Professorin the department of Information Technology. 
He has published paper in international journal.He has participated and 
presented research papers in various national and international seminars and 
conferences. He is an Life member of ISTE. 

2 Dr.R.Lakshimpathi received the B.E degree in 1971 and M.E degree in 
1973 from College of Engineering, Guindy, and Chennai.He received his PhD 
degree in High Voltage Engineering from Indian Institute of Technology, 
Chennai, India. He has 36 years of teaching experience in various Government 
Engineering Colleges in Tamilnadu and he retired as Principal and Regional 
Research Director at Alagappa Chettiar College of Engineering and 
Technology, Karaikudi.He is now working as Professor in Electrical and 
Electronics Engineering department at St.Peters Engineering College, 
Chennai.His areas of research include HVDC Transmission, Power System 
Stability and Electrical Power Semiconductor Drives. 



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A DNA and Amino Acids-Based 
Implementation of Playfair Cipher 



Mona Sabrya), Mohamed Hashemi, Taymoor Nazmyu 

Mohamed Essam Khalifam 

Faculty of Computer Science and information systems, 

Ain Shams University, 

Cairo, Egypt. 

E-mail: mona. sabry @ hotmail.com 



ABSTRACT— The DNA cryptography is a new and very promising 
direction in cryptography research. Although in its primitive stage, 
DNA cryptography is shown to be very effective. Currently, several 
DNA computing algorithms are proposed for quite some 
cryptography, cryptanalysis and steganography problems, and they are 
very powerful in these areas. 

This paper discusses a significant modification to the old Playfair 
cipher by introducing DNA-based and amino acids-based structure to 
the core of the ciphering process. 

In this study, a binary form of data, such as plaintext messages, or 
images are transformed into sequences of DNA nucleotides. 
Subsequently, these nucleotides pass through a Playfair encryption 
process based on amino-acids structure. 

The fundamental idea behind this encryption technique is to enforce 
other conventional cryptographic algorithms which proved to be 
broken, and also to open the door for applying the DNA and Amino 
Acids concepts to more conventional cryptographic algorithms to 
enhance their security features. 

KEY WORDS: DNA, amino acids, encryption, decryption, 
cryptography, security, Playfair cipher. 

I. INTRODUCTION 

As some of the modern cryptography algorithms (such as DES, 
and more recently, MD5) are broken, the new directions of 
information security are being sought to protect the data. The 
concept of using DNA computing in the fields of cryptography 
and steganography is a possible technology that may bring 
forward a new hope for powerful, or even unbreakable, 
algorithms. 

The main purpose behind our work is to discover new fields of 
encoding the data in addition to the conventional used 
encryption algorithm in order to increase the concept of 
confusion and therefore increase security. 



In our work, we applied the conversion of character form or 
binary form of data to the DNA form and then to amino acid 
form. Then the resulting form goes through the encryption 
algorithm which we chose for example; the classical Playfair 
cipher. 

It is Adleman, with his pioneering work [5]; set the stage for 
the new field of bio- computing research. His main idea was to 
use actual chemistry to solve problems that are either 
unsolvable by conventional computers, or require an enormous 
amount of computation. By the use of DNA computing, the 
Data Encryption Standard (DES) cryptographic protocol can be 
broken [6]. In DNA steganography, A DNA encoded message 
is first camouflaged within the enormous complexity of human 
genomic DNA and then further concealed by confining this 
sample to a microdot [3]. Recent research considers the use of 
the Human genome in cryptography. In 2000, the Junior Nobel 
Prize was awarded to a young Romanian American student, 
Viviana Risca, for her work in DNA steganography. [3] 

The one-time pad cryptography with DNA strands, and the 
research on DNA steganography (hiding messages in DNA), 
are shown in [2] and [3]. 

However, researchers in DNA cryptography are still looking at 
much more theory than practicality. The constraints of its high 
tech lab requirements and computational limitations, combined 
with the labor intensive extrapolation means. Thus prevent 
DNA computing from being of efficient use in today's security 
world. 

Another approach is lead by Ning Kang in which he did not use 
real DNA computing, but just used the principle ideas in 
central dogma of molecular biology to develop his 
cryptography method. The method only simulates the 
transcription, splicing, and translation process of the central 
dogma; thus, it is a pseudo DNA cryptography method. [4] 

There is another investigation conducted by [1] which is based 
on a conventional symmetric encryption algorithm called "Yet 
Another Encryption Algorithm" (YAEA) developed by Saeb 
and Baith [1]. In this study, he introduces the concept of using 
DNA computing in the fields of cryptography in order to 
enhance the security of cryptographic algorithms. This is 
considered a pioneering idea that stood behind our work in this 
paper. [1] 



1 



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(2) Information Systems Dept. 
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Although Playfair cipher is believed to be an old, simple and an 
easily breakable cipher, we believe our new modifications can 
make it a more powerful encryption algorithm. This is done by 
introducing concepts of confusion and diffusion to the core of 
the encryption process in addition to preserving the cipher's 
simplicity concept. 

In addition shortage in security features the plaintext message 
is restricted to be all upper case, without J letters, without 
punctuation, or even numerical values. Those problems can be 
easily handled in any modern cipher as handled in our new 
algorithm [8]. 

The character form of a message or any form of an image can 
be easily transformed to the form of bits. This binary form can 
be transformed to DNA form through many encoding 
techniques implemented in previous work and summarized in 
[7]. 

Playfair is based on the English alphabetical letters, so 
preserving this concept, we will use the English alphabet but 
from an indirect way. DNA contains four bases that can be 
given an abbreviation of only four letters (adenine (A), 
cytosine (C), guanine (G) and thymine (T)). On the other side, 
we have 20 amino acids with additional 3 codons to represent 
the Stop of coding region. Each amino acid is abbreviated by a 
single English character. So we are able to stretch these 20 
characters to 26 characters, we will be able to represent the 
English alphabet. 

Then, we have to convert the DNA form of data to amino acid 
form so that it can go through a classical Playfair cipher. 
Through this conversion process, we have to keep in mind the 
problem of ambiguity; that most amino acids are given more 
than possible codon. 

The rest of the paper is organized as follows: section two will 
give a brief explanation of what is DNA and process of 
Transcription and translation. Section three introduces our new 
algorithm followed by a detailed explanation of the 
encryption/decryption processes. Section four shows the 
experiment steps, results. Section five introduces some 
additional security features. Section six shows conclusion and 
future work. 

II. OVERVIEW OF DNA 

A. What is Deoxyribonucleic acid 'DNA '? 

DNA is a nucleic acid that contains the genetic instructions 
used in the development and functioning of all known living 
organisms and some viruses. The main role of DNA molecules 
is the long-term storage of information. DNA is often 
compared to a set of blueprints or a recipe, or a code, since it 
contains the instructions needed to construct other components 



of cells, such as proteins and RNA molecules. The DNA 
segments that carry this genetic information are called genes, 
but other DNA sequences have structural purposes, or are 
involved in regulating the use of this genetic information. 

The DNA double helix is stabilized by hydrogen bonds 
between the bases attached to the two strands. The four bases 
found in DNA are adenine (abbreviated A), cytosine (C), 
guanine (G) and thymine (T). These four bases are attached to 
the sugar/phosphate to form the complete nucleotide, as shown 
for adenosine monophosphate. 

B. The genetic code 

The genetic code consists of 64 triplets of nucleotides. These 
triplets are called codons. With three exceptions, each codon 
encodes for one of the 20 amino acids used in the synthesis of 
proteins. That produces some redundancy in the code: most of 
the amino acids being encoded by more than one codon. 

The genetic code can be expressed as either RNA codons or 
DNA codons. RNA codons occur in messenger RNA (mRNA) 
and are the codons that are actually "read" during the synthesis 
of polypeptides (the process called translation). But each 
mRNA molecule acquires its sequence of nucleotides by 
transcription from the corresponding gene. 

The DNA Codons is read the same as the RNA codons Except 
that the nucleotide thymidine (T) is found in place of uridine 
(U). So in DNA codons we have (TCAG) and in RNA codons, 
we have (UCTG). 

C. Transcription and translation 

A gene is a sequence of DNA that contains genetic information 
and can influence the phenotype of an organism. Within a 
gene, the sequence of bases along a DNA strand defines a 
messenger RNA sequence, which then defines one or more 
protein sequences. The relationship between the nucleotide 
sequences of genes and the amino-acid sequences of proteins is 
determined by the rules of translation, known collectively as 
the genetic code. The genetic code consists of three-letter 
'words' called codons formed from a sequence of three 
nucleotides (e.g. ACT, CAG, TTT). 

In transcription, the codons of a gene are copied into 
messenger RNA by RNA polymerase. This RNA copy is then 
decoded by a ribosome that reads the RNA sequence by base- 
pairing the messenger RNA to transfer RNA, which carries 
amino acids. Since there are 4 bases in 3 -letter combinations, 
there are 64 possible codons (4 3 combinations). These encode 
the twenty standard amino acids, giving most amino acids more 
than one possible codon. There are also three 'stop' or 
'nonsense' codons signifying the end of the coding region; these 



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are the TAA, TGA and TAG codons.RNA codon table, 
WIKIPEDIA: 

http://en.wikipedia.org/wiki/Genetic_code#cite_note- 
pmidl 9056476-8 

III. DNA-B ASED PLAYFAIR ALGORITHM 

A. Encryption algorithm ofDNA-based Play fair cipher: 

Playfair used to be applied to English alphabet characters of 
plaintext. It was unable to encode any special characters or 
numbers which is considered a severe drawback that enforces 
the sender to write everything in the English letters. This 
problem appears while sending numerical data, equations or 
symbols. 

On the contrary, in our algorithm, we can use any numbers, 
special characters or even spaces (not preferred) in or plaintext. 
The encryption process starts by the binary form of data 
(message or image) which is transferred to DNA form 
according to Table 1 . Then the DNA form is transferred to the 
Amino acids form according to Table 2 which is a standard 
universal table of Amino acids and their codons representation 
in the form of DNA [RNA codon table, Wikipedia: 
http://en.wikipedia.org/wiki/Genetic_code#cite_note- 
pmidl 9056476-8 1. 

Note that each amino acid has a name, abbreviation, and a 
single character symbol. This character symbol is what we will 
use in our algorithm. 

Table I: DNA Representation of bits. 



Bit 1 


Bit 2 


DNA 








A 





1 


C 


1 





G 


1 


1 


T 







/ Plaintext 


/ 


/ 




/ 


INPUT 




y Secret Key 






1 


1 




Preprocessing 




Preprocessing 






1 












Convert To Binary 








I 








Convert To DNA 








1 










Con vert To Amino 
Acids 
















X 








> 

3 
tr 
en" 






t 
Playfair 
















I 










Convert To DNA 










1 










• 












OUTPUT 

















Fig. 1: flowchart of the DNA-based Playfair algorithm 

B. Constructing the alphabet table: 

In the table, we have only 20 amino acids in addition to 1 start 
and 1 stop. While we need 25 letters to construct the Playfair 
matrix (note that I/J are assigned to one cell). 

The letters we need to fill are (B, O, U, X, Z). So we will make 
these characters share some amino acids their codons. 
The start codon is repeated with amino acid (M) so we will not 
use it. We will assign to (B) the 3 stop codons. We have 3 
amino acids (L, R, S) having 6 codons. By noticing the 
sequence of DNA of each, we can figure out that each has 4 
codons of the same type and 2 of another type. Those 2 of the 
other type are shifted to the letters (O, U, X) respectively. 
Letter (Z) will take one codon from (Y), so that Y: UAU, Z: 
UAC. Now the new distribution of codons is illustrated in 
Table 3. 



Counting the number of codons of each character, we will find 
the number varies between 1 and 4 codons per character. We 
will call this number 'Ambiguity' of the character [AMBIG]. 

Now we have the distribution of the complete English alphabet, 
so a message in the form of Amino Acids can go through 
traditional Playfair cipher process using the secret key. 



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Table II: Amino acids and their 64 codons 



Ala/A 


GCU, GCC, 
GCA, GCG 


Leu/L 


UUA, UUG, 

cuu, cue, 

CUA, CUG 


Arg/R 


CGU,CGC, 
CGA,CGG, 
AGA,AGG 


Lys/K 


AAA, AAG 


Asn/N 


AAU, AAC 


Met/M 


AUG 


Asp/D 


GAU, GAC 


Phe/F 


UUU, UUC 


Cys/C 


UGU, UGC 


Pro/P 


ccu,ccc, 

CCA, CCG 


Gln/Q 


CAA, CAG 


Ser/S 


UCU, UCC, 
UCA, UCG, 
AGU, AGC 


Glu/E 


GAA, GAG 


Thr/T 


ACU, ACC, 
ACA, ACG 


Gly/G 


GGU,GGC, 
GGA,GGG 


Trp/W 


UGG 


His/H 


CAU, CAC 


Tyr/Y 


UAU, UAC 


Ile/I 


AUU, AUC, 
AUA 


Val/V 


GUU, GUC, 
GUA, GUG 


START 


AUG 


STOP 


UAA, UGA, 
UAG 



Table III: New distribution of the alphabet with the corresponding new codons: 





STOP 




















from 






To 






from 


from 




to 






to 


from 


To 


A 


B 


C 


D 


E 


F 


G 


H 


. ■ K 


L 


M 


N 


O 


P 


Q 


R 


S 


T 


U 


V 


W 


X 


Y 


Z 


4 


3 


2 


2 


2 


2 


4 


2 


3 




2 


6 


1 


2 




4 


2 


6 


6 


4 




4 


1 




2 




GCU 


UAA 


UGU 


GAU 


GAA 


UUU 


GGU 


CAU 


AUU 




AAA 


UUA 


AUG 


AAU 


UUA 


ecu 


CAA 


CGU 


UCU 


ACU 


AGA 


GUU 


UGG 


AGU 


UAU 


UAC 


GCC 


UAG 


UGC 


GAC 


GAG 


UUC 


GGC 


CAC 


AUC 




AAG 


UUG 




AAC 


UUG 


ccc 


CAG 


CGC 


UCC 


ACC 


AGG 


GUC 




AGC 


UAC 




GCA 


UGA 










GGA 




AUA 






CUU 








CCA 




CGA 


UCA 


ACA 




GUA 










GCG 












GGG 










cue 








CCG 




CGG 


UCG 


ACG 




GUG 
































CUA 












AGA 


AGU 






































CUG 












AGG 


AGC 
















4 


3 


2 


2 


2 


2 


4 


2 


3 


3 


2 


4 


1 


2 


2 


4 


2 


4 


4 


4 


2 


4 


1 


2 


1 


1 



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Table IV: New Distribution for codons on English alphabet 



A 


GCU, GCC, 
GCA, GCG 


W/a 


^ 


W/a 


^^ 


K 


AAA, AAG 


N 


AAU, AAC 


M 


AUG 


D 


GAU, GAC 


F 


UUU, UUC 


C 


UGU, UGC 


P 


CCU,CCC, 
CCA, CCG 


Q 


CAA, CAG 


^ 


^ 


E 


GAA, GAG 


T 


ACU, ACC, 
ACA, ACG 


G 


GGU,GGC, 
GGA,GGG 


W 


UGG 


H 


CAU, CAC 


SXXWJ 


NfcNW 


1 


AUU, AUC, 
AUA 


V 


GUU, GUC, 
GUA, GUG 


B 


UAA, UGA, 
UAG 


O 


UUA, UUG 


U 


AGA,AGG 


X 


AGU,AGC 


Z 


UAC 







of plaintext in DNA form which can be transferred to binary 
form and then the final character form. 

D. Pseudo-code 

Input: 

[P] Plaintext (characters with spaces, numbers or any 
special characters). 

[K] Secret key (English characters without any 
number or special characters). 

Algorithm body: 

Preprocessing: 

1- Prepare the secret key: 

- Remove any spaces or repeated characters from [K]. 

- Put the remaining characters in the UPPER case 
form. [K]^UPPER[K]. 



The output form is the amino acid form of cipher text. DNA 
form of cipher text can be demonstrated also from Table 4 by 
choosing random codons accompanied to each character. The 
concept that one character can have more than one DNA 
representation is itself an addition to confusion concept that 
enhances the algorithm strength. Table\ IV shows the new 
distribution of codons on the amino acids and additional 
alphabetical English letters according to our algorithm. 

C. Decryption and Ambiguity problem 

The decryption process is simply the inverse of the encryption 
process unless that we will find a problem in constructing the 
DNA form of plaintext from the amino acid form which is of 
length (L). The problem is that we are unable to choose which 
codon to put in accordance to each amino acid character. This 
is simply the problem of codon-amino acid mapping problem 
arised with other algorithm based on the concept of Central 
Dogma like [4]. The way Nang handled this problem is to put 
this codon-amino acid mapping in the secret key to be sent 
through a secure channel [4] . This idea is not efficient since it 
increases the size of the key in relation to size of the plaintext. 

The solution in our algorithm is located in two additional bits 

for each amino acid character to demonstrate which codon to 

choose. 

We said before that each amino acid has 1 , 2, 3 or 4 codons to 

represent it. This is a number that can be put in 2 bits from 

0^3. 

These 2 bits can be converted to DNA form from Table 1. 

That is why the final cipher text is both the DNA form of 
cipher text of length (3L) and the array carrying the ambiguity 
of length (L). 

In decryption, the amino acid form of plaintext with the 
assistance of the ambiguity array can construct the correct form 



2- Prepare the plaintext: 
- Remove the spaces from [P] (done to avoid 
attacker's trace to a character which is repeated many 
times within the message) 

Processing: 

1- Binary form [BP] = BINARY [P] (Replace each 
character by its binary representation-8 bits-) 

2- DNA form [DP] = DNA [BP] (Replace each 2 bits 
by their DNA representation) 

3- Amino acids form [AP] = AMINO [DP] (Replace 
each 3 DNA characters by their Amino acid character 
keeping in track the ambiguity of each Amino acid 
[AMBIG]. 

4- Construct the Playfair 5X5 matrix and add [K] row 
by row, then add the rest of alphabet characters. 

5- Amino acid of cipher text [AC]= PLAYFAIR [AP]. 

6- DNA form of cipher text [DC] = DNA [AC]. 



Output: 



Add [DC] and [AMBIG] together in the suitable 
form-> final cipher text [C]. 



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E. Samples of the program steps and output 



Ambiguity 

Embedded Ambiguity 
O After Ambiguity 



Alphabet Distribution 



LlZI 
O Protein 



Key EGYPT VICTORY 



Plaintext 

Binary 

DNA 

Proteins 

After Playfair 

Ambiguity 

DNA 



attack starts at 2:00 PM. 



RPHSRPIGLSHDLAHYRPQBMDNOITMXACE 



OVAZOVCEKt AFKBDVOVLHWMXFCOKOSFIG 



20122310201120102001001121110010 



UUA G GIT A GCU C I AC G UUA G GIT U UGU C GA4 A AAA G AGA A GCL" C ITU C AAA G LAA A GAU C GIT A LTA G GIT A CUU A CAU 

Cipbertext c ugg a aug a agu c uuu c ugu g uua c aaa c uua c ucxj a uuu a auu c ggu a 



Figure 2: sample of steps of encryption implementation 



Ambiguity 
O After Ambiguity 



Alphabef Distribution 

Englisl 
O Protein 



Alpnanet 
English 



Key EGYPT VICTORY 



Cipbertext 
DNA 

Ambiguity 
Proteins 

After Playfair 

DNA 

Binary 



20122310201120102001001121110010 



OVAZOVCEKl AFKBDVOVLHWMXFCOKOSFIG 



RPHSRPIGLSHDLAHYRPQBMDNOITNIXACE 



a 1 1 a c k s t a r t s a t 2 : - P M . 



Figure 3: sample of steps of decryption implementation 



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IV. EXPERIMENT AND PERFORMANCE ANALYSIS 

A. Experiment 

1- Experiment inputs and attributes 

We led our experiment on the famous novel 'A Tale of Two 
Cities' by Charles Dickens found on 

http://www.literature.org/authors/dickens-charles/two-cities/ . 
We will take paragraphs from the beginning of the novel 
according to the estimated storage size in Kilobytes (from 1 
KB and increasing till 150 KB). 

2- System Parameters 

The experiments are conducted using Intel(R) Core (TM) 
2CPU T5300, 1.73 GHz, 32 bit processor with 1GB of 
RAM. The simulation program is compiled using the default 
settings in .NET 2005 visual studio for C# windows 
applications under WINDOWS XP as the operating system. 
The experiments will be performed several times to assure that 
the results are consistent and valid. 

3- Experiment Factors 

The chosen factor here to determine the performance is the 
algorithm's speed to encrypt data blocks of various sizes. 
Suppose we will use the original sequence of English alphabet 
and embed the ambiguity inside the message not after it. 
The secret key used is "CHARLES DICKENS" which results 
in 11 Bytes key. 



4- Experiment steps: 
Experiment preprocessing: 

1- Loading the table of the 64 amino acids with their 
DNA Encodings and number of ambiguous encodings. 

2- Formatting the secret key by removing spaces, 
repeated characters and non English letters. 

3- Formatting the plaintext by removing spaces between 
words and separating the repeated doubles by the 
character '-' which chosen to be a rarely used 
character. 

Processing: 

This includes: 

1- Converting characters to binary form. 

2- Converting binary to DNA 

3- Converting the DNA to amino acids and recording 
ambiguity. 

4- Do Playfair encryption. 

5- Convert the amino acid form of cipher text to DNA 
form in addition to embedding the ambiguity in the 
DNA format. 

5- Experiment Results 

The next table illustrates the experiments and time taken to 
encrypt each piece of plaintext (each is of different data loads) 
in milliseconds. 

The time taken by loading the amino acids table and preparing 
the secret key is ignored because it is comparatively small to 
processing time. 



Table 4: performance results of DNA-based Playfair algorithm 



Input size of 
plaintext 
(in KB) 


Plaintext after 
preprocessing 


Preprocess 

ing 
plaintext 


From Binary 

to Amino Acids 

form 


playfair 


Prepare 
ciphertext 


Total 

processing 

time 


Bytes/Second 


1 (1,022 B) 


846B 











15.625 


15.625 


65.408 


10 (9,757 B) 


8124B 


62.500 


15.625 





125.000 


203.125 


48.034 


20 (20,023 B) 


16599B 


203.125 


15.625 





171.875 


390.625 


51.259 


50 (50,432 B) 


41781B 


1062.500 


46.875 


15.625 


437.500 


1562.500 


32.276 


100 (97,072 B) 


83910B 


4687.500 


78.125 


31.25 


859.375 


5656.250 


17.162 


150 (153,418 B) 


127098 B 


11390.625 


140.625 


31.25 


1343.750 


12906.25 


11.887 



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V. Additional security features 

We have illustrated the main core of the algorithm and now we 
are going to suggest some additional features to the algorithm 
which can enhance its security and strength. 

A- The key: 

It is quite clear that the more random and long the key is, the 
more the difficulty to break the cipher will be. 

B- Use Amino acids alphabet sequence instead of English 
alphabetical sequence: 

The standard table of amino acids has a special sequence 
defined in the matrix [4X4] (UCAG) X (UCAG). This 
sequence of acids can be used instead of the sequence of 
English alphabet letters to fill the rest of the 5X5 matrix after 
adding the secret key. 

C- Combine the total resulting message into long strand of 
DNA to be inserted in a microdot (steganography): 

One of the advantages of this algorithm is the variety of ways 
we can use to write down the cipher text. It can be written in 
DNA form, binary form or even character form which is more 
confusing. The advantage of DNA form is that it can make use 
of several steganography techniques developed for DNA 
messages [3]. It can also be prepared in biological labs like in 
[2] in which DNA message goes through a biological DNA 
encryption process using one time pad or substitution. 

D- Ambiguity a problem that contains useful confusion 
feature: 

Some characters in table 2 can have 6 codons representing the 
problem of ambiguity. The way we handled the preparation of 
table 3 made each character have in maximum 4 codons. The 
number 4 can be represented by 2 bits and therefore ban be 
represented by one DNA character. That was a benefit that 
made us able to write the cipher text with ambiguity in the 
form of DNA. 

E- Use of conventional XOR-ing procedure: 

Another way to increase security is defining another key that 
can be XOR-ed with the amino acid form or DNA form of 
cipher text. It was a pioneer idea by [1] to choose the key as the 
DNA strand of a certain organism. This idea assures the key 
randomness and variety in length according to the length of the 
message. 

VI. CONCLUSION AND FUTURE WORK 

The fundamental idea behind this technique is to open the door 
for the idea of applying the DNA and Amino Acids encoding 
concepts to other conventional cryptographic algorithms to 
enhance their security -vulnerability- features. 
Our algorithm initially succeeded in overcoming some main 
problems in "Playfair cipher" like restriction of plaintext to 
"English Alphabet". As in our algorithm the plaintext is to be 



converted to its binary value before encryption, it now clear 
that the plaintext message can be written in upper or lower 
case, with any punctuation, and numerical values. 

Other papers conducted the idea of amino acids way of 
representation from the point of view of the central dogma 
design [4]. But they were unable to clearly handle the problem 
of ambiguity as performed by our algorithm. Our algorithm 
made few preprocessing steps to handle this problem and the 
result was quite accurate (same input message obtained after 
decryption). This feature is very important when regarding an 
encryption algorithm in order to verify the concept of data 
integrity or in other words, to assure that data after decryption 
to be the same input data before encryption. 
Finally, our algorithm provides different forms of the cipher 
text like: Binary form, DNA form, Amino Acid form or 
character form. Those various forms can match different used 
applications. 

Our future work is dedicated to implementing this encoding on 
other known algorithms and measuring its performance and 
security, Also, Experiments should be conducted to implement 
the algorithm on different applications to ensure its feasibility 
and applicability. 



REFERENCES 

[1] Sherif T. Amin, Magdy Saeb, Salah El-Gindi, "A DNA-based 
Implementation of YAEA Encryption Algorithm," IASTED 
International Conference on Computational Intelligence (CI 2006), San 
Francisco, Nov. 20, 2006. 
http://www.actapress.com/PaperInfo.aspx?PaperID=29058 . 

[2] Ashish Gehani, Thomas LaBean and John Reif. DNA-Based 
Cryptography. DIMACS DNA Based Computers V, American 
Mathematical Society, 2000. 

[3] TAYLOR Clelland Catherine, Viviana Risca, Carter Bancroft, 1999, 

"Hiding Messages in DNA Microdots". Nature Magazine Vol.. 399, June 
10, 1999. 

[4] KANG Ning, "A Pseudo DNA Cryptography Method", Independent 
Research Study Project for CS5231, October 2004. 

[5] Leonard Adleman. "Molecular Computation of Solutions to 

Combinatorial Problems". Science, 266:1021-1024, November 1994. 

[6] Dan Boneh, Cristopher Dunworth, and Richard Lipton. "Breaking DES 
Using a Molecular Computer". Technical Report CS-TR-489-95, 
Department of Computer Science, Princeton University, USA, 1995. 

[7] Dominik Heider and Angelika Barnekow, "DNA-based watermarks 
using the DNA-Crypt algorithm", Published: 29 May 2007 BMC 
Bioinformatics 2007, 8:176 doi:10.1 186/1471-2105-8-176, 
http://www.biomedcentral.eom/1471-2105/8/176 ,© 2007 Heider and 
Barnekow; licensee BioMed Central Ltd. 

[8] William Stallings. "Cryptography and Network Security", Third 
Edition, Prentice Hall International, 2003. 



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Vol. 8, No. 3,2010 



Ultra Wideband Slot Antenna with Reconfigurable 

Notch bands 



J. William* and R.Nakkeeran 

Department of Electronics and Communication Engineering 

Pondicherry Engineering College 

Puducherry, India . 605014. 

Email id: wills.susan@gmail.com, rnakeeran@pec.edu 

^Corresponding author: wills.susan@gmail.com 



Abstract — An Ultra Wideband (UWB) slot antenna with 
reconfigurable notch bands is presented in this paper. The basic 
UWB antenna consists of a rectangular slot with triangular 
structure that acts as a tuning stub with CPW feed. The CPW 
feed is designed for 50 il impedance. The notch band is achieved 
by inserting a rectangular slot in the ground plane with a 
effective length of X/2. The reconfigurable rejection of the bands 
3.1 GHz - 3.9 GHz, 4 GHz-5.3 GHz and 4.1 GHz-5.9 GHz are 
achieved by switching the diodes placed over the slot in the 
ground plane. The characteristics of the designed structure are 
investigated by using MoM based electromagnetic solver, IE3D. 
The return loss (S n ) of the antenna is measured and that are 
comparable with the simulation results. The proposed antenna 
covers the entire UWB range 3.1 GHz to 10.6 GHz with 
reconfiguration. The low profile and simple configuration of the 
proposed antenna leads to easy fabrication that may be built in 
any wireless UWB device applications where reconfigurable 
rejection bands are required. The rejection of WiMax, IEEE 
802.11a and HYPERLAN/2 bands can be achieved by using the 
proposed antenna design. 

Keywords- coplanar waveguide; notch band; slot antenna; 
reconfiguration; ultra wideband 

I. Introduction 

In the year 2002, Federal Communications Commission 
(FCC) released the unlicensed UWB spectrum 3.1 GHz to 10.6 
GHz for the commercial purposes. After the release of UWB, it 
gains much attention by the researchers due to its inherent 
properties of low power consumption, high data rate and 
simple configuration [1]. With the rapid developments of such 
UWB systems, a lot of attention is being given for designing 
the UWB antennas. Designing an antenna to operate in the 
UWB band is quiet challenging one because it has to satisfy the 
requirements such as ultra wide impedance bandwidth, omni 
directional radiation pattern, constant gain, high radiation 
efficiency, constant group delay, low profile, easy 
manufacturing etc [2]. Interestingly the planar slot antennas 
with CPW fed posses the above said features with simple 
structure, less radiation loss, less dispersion and easy 
integration of monolithic microwave integrated circuits 
(MMIC) [3]. Due to inherent nature of UWB system sharing 
the same spectrum with other systems. The interference from 
other system should be considered when you design a UWB 



system. For example, the other wireless communication 
systems such as WiMax (3.3 GHz - 3.7 GHz), IEEE 802.11a 
(5.15 GHz - 5.35 GHz and 5.725 GHz - 5.825 GHz) and 
HYPERLAN/2 (5.15 GHz - 5.35 GHz and 5.47 GHz - 
5.825 GHz) which are also operated in the portion of UWB 
band. Therefore, the UWB antenna design could play an 
important role when interference with other wireless 
applications such as WiMax, IEEE 802.11a and HYPERLAN/2 
systems which are also coexisting with UWB band (3.1 GHz - 
10.6 GHz), which degrades the UWB system performance. 
Therefore, a UWB antenna having reconfigurable frequency 
band notch characteristics is enviable. 

To mitigate the above electromagnetic interference from 
nearby communication systems many antenna designs have 
been proposed in the literature [4-13]. Many techniques also 
used to introduce notch band for rejecting the interference in 
the UWB slot antennas. It is done either by inserting half 
wavelength slits, stripes in the tuning stub [14], or inserting 
stub in the aperture connected to the ground planes [15], or 
inserting square ring resonator in the tuning stub [16], or 
inserting 'L' branches in the ground plane [17], or with 
complementary split ring resonator [18], or inserting strip in the 
slot [19]. All the above methods are used for rejecting a fixed 
band of frequencies. But to effectively utilize the UWB 
spectrum and to improve the performance of the UWB system, 
it is desirable to design the UWB antenna with reconfigurable 
notch band. It will help to minimize the interference between 
the systems and to improve the performance of the UWB 
systems. In general, reconfiguration are popular in antenna 
engineering for their frequency agility, bandwidth enhancement 
and polarization diversity [20-23]. In [24] RF MEMS switches 
are used for the reconfigurabilty of rejection band between the 
frequencies 5 GHz to 6 GHz. In this paper a new method is 
proposed to obtain the reconfiguration in the frequency notch 
bands for WiMax, WLAN 802.1 la and HYPERLAN/2 systems 
by making 'on' and 'off the diodes placed over the slot that is 
introduced on the ground plane. This intern changes the tuning 
length of the slot, which is responsible for the desired 
frequency notch band. Through our simulation it is also found 
that the desired frequency notch band can be obtained by 
inserting slot in the tuning stub with appropriate length rather 
than on the ground plane. However this paper mainly focuses 



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on reconfigurable notch band through the introduction of slot in 
the ground plane. The simulation software used for this 
analysis is IE3D [25]. The paper is organized as follows: 
Section II brings out the geometry of the antenna. In section 
III, simulation results and analysis are presented. Obtained 
experimental results are given in Section IV. Section IV 
concludes the work. 




2.4 mm 

0,5 mm 



Substrata h=1.6miiL l Ej-=4.4 



t Z 



Fig. 1 Geometry of the proposed slot antenna with biasing circuit. 

II. Antenna Geometry 

The structure of the antenna is shown in Fig 1 . The antenna 
consists of rectangular slot with width 'Wi' and length 'Li'. 
The tuning stub comprises a triangular patch with height 'H'. 
The distance between the tuning stub and feed line is 'd', 'W 
and 'L' are the overall width and length of the antenna 
respectively. In this study, the dielectric substance (FR4) with 
thickness of 1.6 mm with relative permittivity of 4.4 is chosen 
as substrate to facilitate printed circuit board integration. The 
CPW feed is designed for 50 f2 characteristic impedance with 
fixed 2.4 mm feed line width and 0.5 mm ground gap. Slits are 
introduced to avoid the short circuit between the ground 
planes. Fast switching diode LL4148 is used as a switching 
device. The proposed antenna is designed to cover the entire 
UWB band with reconfiguration capability. The placement of 
the diodes 'L d ' are desired by the effective wavelength X&, 
The effective wavelength of the slot is, 




S+\ 



'eff 



(1) 



The placement of the diodes 'L d ' 



Vol. 8, No. 3, 2010 



I, = 



(2) 



where %' is the centre frequency of the notch band. The 
placement of the diodes is desired by the effective wavelength 
for the different notch frequencies. 

III. Simulated Results and Analysis 

The analysis and performance of the proposed antenna is 
explored by using IE3D for the better impedance matching. 
The detailed parametric analysis of the UWB antenna is 
carried out and presented in our paper [26]. In order to 
evaluate the performance of the proposed antenna with 
reconfigurable slot in the ground plane, the optimal parameter 
values of the antenna (without slot) suggested in that paper are 
considered. The final optimal parameter values of the antenna 
are listed in the Table 1 . However to study the impact of the 
slot, the slot length 'L 2 ' and width 'W 2 ' are varied by keeping 
one of them as constant. In the simulation switching diodes 
were simulated as capacitor for the 'Off state and as a resistor 
in the 'On' state. The current distribution, gain and group 
delay are also studied. The simulated return loss, with 
different states of the diode are shown in Fig. 2, clearly 
indicate that the notch frequency and notch bandwidth is 
varied when the slot length is varied. 



TABLE I. 



Optimal Parameter Values of the Antenna 



Parameter 


Description 


Optimal 
Value 


L 


Length of the antenna 


28 mm 


W 


Width of the antenna 


21 mm 


Li 


Length of the slot 


15 mm 


Wi 


Width of the slot 


16.8 mm 


d 


Feed gap distance 


1.6 mm 


H 


Height of the patch 


9.3 mm 




3 4 5 6 7 8 9 10 11 12 
Frequency in GHz 

Fig. 2 Simulated response curves for different biasing condition. 



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A. Effect of Slot in the Ground Plane 

The length of the ground slot is adjusted by switching the 
diodes placed appropriately over the slot. The switching 
diodes 'Di' and 'D 2 ' are placed over the slot along the ground 
plane. The Table 2 shows the states of the diode switches and 
notch frequency with notch band width. 



TABLE II. 



Description of Switching of the Diodes 



D x 


D 2 


Notch Freq. (GHz) 


Notch band 


Off 


Off 


3.6 GHz 


3.1-3.9 GHz 


Off 


On 


5.1GHz 


4 -5.3 GHz 


On 


Off 


5.6 GHz 


4.1-5.9 GHz 



When both diodes are in open state the effective slot 
length is longer to achieve the notch frequency at 3.6 GHz 
with frequency notch band of 3.1 GHz to 3.9 GHz, which is 
the WIMAX band of frequency. When D x is 'Off and D 2 is 
'On' the length of the slot is tuned to eliminate the band of 
4 GHz to 5.3 GHz with notch center frequency of 5.1 GHz 
which covers the lower band of IEEE 802.11a. When D x is 
'On' and D 2 is 'Off the effective wavelength of the slot is 
adjusted such that to eliminate the upper band of IEEE 
802.11a with a notch frequency of 5.6 GHz with the notch 
frequency band of 4.1 GHz to 5.9 GHz. 

B. Simulated Current Distribuition 

The simulated current distribution at different notch 
frequencies according to the switching condition of the diodes 
is presented in Fig.3, it is witnessed from the figure, the current 
distribution around the radiation slot is disturbed by the 
introduction of ground slot, which is responsible for the notch 
in the frequency band. If the slot length is longer the destructive 
interference with the main radiating slot is high which causes 
the notching in that band is good. When the ground slot length 
is decreased, the current distribution of the radiating slot is 
disturbed by the tuning slot is less effective compared with the 
longer slot size. 




(a) (b) (c) 

Fig. 3 Simulated current distribution for different switching conditions of 
diodes, a) 'Di and 'D 2 ' 'Off b) TV 'On' and 'D 2 ' 'Off c) T>i' 'Off and 
'D 2 ' 'On' 

C. Gain 

The computed gain of the proposed UWB antenna for 
different tuning lengths of the slot in the ground plane and 
without slot is compared in Figure 4. 




■ without slot in the ground pla 
- D1,D2=off 

j ...... D1= on & D2= off 1 ;- 

i D1=off&D2=on 

J I I I I I L 



3 4 5 6 7 8 9 10 11 12 

Frequency in GHz 

Fig. 4 Simulated gain for different biasing conditions of diodes. 

It is observed that there is a gain variation at the notched 
frequencies when the length of the slot is tuned, which is 
-7.5 dBi for both diodes are 'off state and 0.4 dBi when D x is 
'Off and D 2 is 'On'. Where as it is 0.6 dBi for D x is 'On' and 
D 2 is 'Off. The antenna without slot and the gain varies from 
2 dBi to 6 dBi across the UWB spectrum. 

D. Radiation pattern 

The radiation pattern for the E plane and H plane at 
frequencies 3.4 GHz, 5.2 GHz and 5.6 GHz are simulated for 
all the three cases of diode states are compared and displayed 
in Fig.5, which disclose that the directivity gain of the 
radiation pattern is reduced at the notch frequencies without 
affecting the shape of the radiation pattern. In the E plane, it is 
bidirectional pattern and in H plane, it is omni directional 
pattern. 



D1 off D2off 
D1 off D2 on 
D1 on D2 off 




D1off D2off 
D1 off D2 on 
D1 on D2 off 



(b) 5.2 GHz 



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D1 off D2 off 
--D1 off D2on 
— D1onD2off 




D1off D2off 

- — -D1 off D2on 

— D1 on D2 off 



(c) 5.6 GHz 
Fig. 5 Simulated E-plane and H-plane radiation patterns at notch frequencies. 

E. Group delay 

The group delay V of the antenna is calculated from the 
phase of the computed 'S 2 i' by using the following equation 
and plotted in Fig. 6, 

x = -^ (2) 

df 

where ' (j) ' is phase of S 2 i in radians /sec and T is frequency 
in GHz. From the Fig. 6, it is noticed that the variation in the 
group delay for the antenna with and without slot is around 2 
ns for the frequency range from 3.1 GHz to 10.6 GHz. There 
is a variation in the group delay at the notch band in the 
response which is due to notch behavior of the antenna. 



E-*t 



a 



«4 






-10 





1 ; ; ill; _ 




Mf 


"p^H^^w^ 




■--r-- ! --- ! ---¥f- ! --- 


1 ; 


: i i ! i 




^— D1 off D2 off 
■ ; D1 off D2 on 


i i 


---D1 onD2 off 

i i i i i 



G 7 8 9 10 

Frequency in GHz 

Fig. 6 Group delay response. 



11 12 



IV. EXPERIMENTAL!. RESULTS AND DISCUSSION 

The prototype of the proposed antenna shown in Fig. 1 was 
fabricated for different parameters with their optimal values 
and tested. Using Hewlett Packard Network Analyzer 
(HP8757D), the VSWR is measured and compared with the 
simulation result is shown in Fig.7. There is a discrepancy 
between the measured and the simulated ones might be the 
effect of soldering the SMA connector or fabrication 
tolerance. The simulation result was obtained by assuming 
coplanar as input port, whereas practically SMA connector 
was used, the imperfect transition between SMA feed to 
coplanar may introduce losses [27] and also the capacitances 
can lead to shift in the frequency. 



EQ 

"P 

c 

(A 
§ 



1 

m 




-25 



-30 



D1 off D2 off 
D1 off D2 on - 
D1 on D2 off - 



- Measured 
-Measured 
-Measured 



- Simulated 

■Simulated 

Simulated 



4 S S 7 8 9 10 11 

Frequency in GHz 

Fig. 8 Comparison of measured and simulated 
VSWR. 

V. Time Domain Analysis 

In ultra wideband systems, the information is transmitted 
using short pulses. Hence, it is important to study the temporal 
behavior of the transmitted pulse. The communication system 
for UWB pulse transmission must provide as minimum as 
possible distortion, spreading and disturbance. The channel is 
assumed to be linear time invariant (LTI) system to verify the 
capability of the proposed antenna for transmission and 
reception of these narrow pulses. The transfer function of the 
entire system is computed using simulated value of 'S 2 i' 
parameter [28]. The received output pulse is obtained by taking 
the Inverse Fourier Transform (IFT) of the product of transfer 
function and spectrum of the test input pulse. While computing 
'S 2 i', two identical antennas are placed face to face at a 
distance of 75 cm that is greater than the far-field distance of 
the antenna. The cosine modulated Gaussian pulse is 
considered for this analysis with centre frequency of 6.85 GHz 
and pulse width of 220 picoseconds, whose spectrum is shown 
in Fig.7. It satisfies the requirement of FCC mask for UWB 
indoor emission. The comparison of input and output responses 
of the system for the antenna with different notches are shown 
in Fig. 8, which ensures the distortion less pulse transmission 
and also guarantees that the designed antenna is capable of 
transmitting and receiving short pulses. 




9 "5 

2 4 6 8 10 12 ' ,. . 

Frequency in GHz Time in nanosec. 

(a) (b) 

Fig. 7. a) Spectrum of the test input pulse with FCC mask 
b) input pulse in time domain 



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0.8 
0.6 
0.4 
0.2 


-0.2 
-0.4 
-0.G 
-0.8 
-1 




Input pulse 

— — - Received pulse-D1 Off D 2 off 
Received pulse-D1 Off D 2 on 

— ■ — Received pulse-Pi on P2 off 



1.5 



3.5 



2 2.5 3 

Time in ns 

Fig. 8. Comparison of input pulse and received pulses in time domain. 

VI. Conclusion 

This paper describes the simulated analysis of UWB slot 
antenna with reconfigurable rejection bands. By switching the 
diodes, the effective length of the ground slot is tuned to the 
notch center frequency. The simple configuration and low 
profile of the proposed antenna leads to easy fabrication that 
may be built for any wireless UWB device applications where 
reconfigurable rejection of notch bands is required. The 
rejection of WiMax, IEEE 802.11a and HYPERLAN/2 bands 
can be achieved using the proposed antenna. 

References 

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Systems in Communication Engineering, New York: John Wiley and 
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[4] I. -J. Yoon, H. Kim et al, "Ultra- wideband tapered slot antenna with band 
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[5] A. M. Abbosh, M. E. Bialkowski et al, " A Planar UWB antenna With 
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[6] Keyvan Bahadori and Yahya Rahmat-Samii, "Miniaturized Elliptic- 
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[7] Chong-Yu Hong, Ching-Wei Ling et al, " Design of a Planar Ultra- 
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AUTHORS PROFILE 

J.William received his B.E. degree in Electronics and Communication from 
Bharathidasan University, Tamilnadu, India, and the M. Tech. degree in 
Communication Systems from National Institute of Technology (N.I.T), 
Trichy,India, in 1991 and 2006 respectively. He is currently working towards 
the Ph.D. degree at Pondicherry Engineering College, Pondicherry, India. He 
is a life member of ISTE and IE (I) and member of IEICE and EurApp. His 
current research interest is in the area of coplanar waveguide feed antennas 
and printed slot antennas for UWB. 

R. Nakkeeran Received BSc. Degree in Science and B.E degree in 
Electronics and Communication Engineering from the Madras University in 
1987 and 1991 respectively and M.E degree in Electronics and 
Communication Engineering (diversification in Optical Communication) from 
the Anna University in 1995. He received Ph.D degree from Pondicherry 
University in 2004. Since 1991, he has been working in the teaching 
profession. Presently, he is Assistant Professor in Pondicherry Engineering 
College. He is life member of IETE, ISTE, OSI and IE(I). Also he is member 



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Vol. 8, No. 3, 2010 
of OS A, SPIE and IEEE. He has published seventy five papers in National and 
International Conference Proceedings and Journals. He has co-authored a 
book, published by PHI. His areas of interest are Optical Communication, 
Networks, Antennas, Electromagnetic Fields and Wireless Communication. 



1 39 http://sites.google.com/site/ijcsis/ 

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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3,2010 



UWB Slot Antenna with Rejection of IEEE 802.11a 

Band 



J. William* and R.Nakkeeran 

Department of Electronics and Communication Engineering 

Pondicherry Engineering College 

Puducherry, India . 605014. 

Email id: wills.susan@gmail.com, rnakeeran@pec.edu 

^Corresponding author: wills.susan@gmail.com 



Abstract — A compact coplanar waveguide (CPW) fed slot 
antenna for ultra wideband (UWB) with notched band from 
5.1 GHz to 5.9 GHz is presented in this paper. By inserting a 
rectangular slot with the particular length and width in the 
ground plane, a desired notch in the frequency band can be 
achieved. The characteristics of the designed structure are 
investigated using an electromagnetic solver, IE3D. The overall 
size of the antenna comes around 28(L) x 21(W) x 1.6(T) mm 3 . 
For the developed antenna the VSWR is measured and compared 
with the simulated results. The measured parameter is in good 
agreement with the simulation and the antenna covers entire 
UWB band ranging from 3.1 GHz to 11.4 GHz with band 
notching between 5.1 GHz and 5.9 GHz. Time domain analysis of 
the antenna is also investigated and presented, which ensures that 
the antenna is capable of working effectively in the UWB 
environment. This type of antenna configuration would be quiet 
useful for UWB indoor applications with no interference from 
WLAN and HYPERLAN/2 systems when they coexist. 

Keywords- band notch; coplanar waveguide; slot antenna; time 
domain analysis; ultra wideband 

I. Introduction 

To support variety of applications to the mobile users needs 
wireless communication systems with higher capacity and data 
rate. Ultra wideband (UWB) is a short pulse communication 
system offers both of the above requirements. After the release 
of UWB by Federal Communications Commission (FCC), it 
has become one of the interesting technologies in indoor 
wireless communications system and receives much attention 
by the industries and academia due to its properties of low 
power consumption, support of high secured data rate and 
simple configuration [1]. Designing an antenna to operate in 
the UWB band is quiet challenging one because it has to satisfy 
the requirements such as ultra wide impedance bandwidth, 
omni directional radiation pattern, constant gain, high radiation 
efficiency, constant group delay, low profile, compact and easy 
manufacturing [2]. Interestingly the planar slot antennas with 
CPW fed possesses the above said features with simple 
structure, less radiation loss, less dispersion and easy 
integration of monolithic microwave integrated circuits 
(MMIC) [3]. Most of the UWB antennas have a wide 
bandwidth, covering bands used for other wireless 
communication applications. Therefore, the UWB antenna 



design could play an important role when interference with 
other wireless applications such as IEEE 802.11a and 
HYPERLAN/2 systems which are also coexisting with UWB 
band (5.1 GHz - 5.9 GHz), which degrades the UWB system 
performance. 

Therefore, a UWB antenna having frequency band notch 
characteristics is enviable. To mitigate this electromagnetic 
interference from nearby communication systems many 
antenna designs have been proposed in the literature [4-10]. 
Many techniques also used to introduce notch band for 
rejecting the interference in the UWB slot antennas. It is done 
either by inserting half wavelength slits, stripes in the tuning 
stub [11], or inserting stub in the aperture connected to the 
ground planes [12], or inserting square ring resonator in the 
tuning stub [13], or inserting 'L' branches in the ground plane 
[14], or with complementary split ring resonator [15], or 
inserting strip in the slot [16]. In this paper, a new type of 
UWB antenna and its notched design is proposed by inserting a 
slot in the ground plane at a half wavelength size at the desired 
center notch frequency of 5. 5 GHz, which is an average of 
notch band 5.1 GHz to 5.9 GHz. The proposed antenna is 
simulated and analyzed by using simulation software, IE3D 14 
[17]. The details of the proposed design and its experimental 
results are presented and discussed in the following sections. 
The paper is organized as follows: Section II brings out the 
design and geometry of the antenna. In Section III simulation 
results and analysis are presented. Obtained experimental 
results are given in Section IV. Section V explains time domain 
analysis of the antenna. Section VI concludes the paper. 

II. Antenna Geometry 

In designing this type of antennas, the width 'W' and 
length 'L' play a crucial role in determining the resonant 
frequency of the system. The initial values of these parameters 
are calculated by using the equations given in [18] for the 
substrate height (h), dielectric constant (s r ) and for the lower 
frequency. The designed values of the antenna are optimized 
with IE3D tool. The optimization was performed for the best 
impedance bandwidth. The proposed antenna with slot in the 
ground plane in this paper is a dimensional modification of 
structure given in [19]. The notched design for the particular 



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band is obtained by introducing a slot in the ground plane 
nearer to the radiating slot. 



W 




(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3,2010 
this study, the dielectric substance (FR4) with thickness of 
1.6mm with relative permittivity of 4.4 is chosen as substrate to 
facilitate printed circuit board integration. The CPW feed is 
designed for 50 £1 characteristic impedance with fixed 2.4 mm 
feed line width and 0.5 mm ground gap. 

III. Simulated Results and Analysis 

The analysis and performance of the proposed antenna is 
explored by using IE3D for the better impedance matching. 
The parametric analysis of the antenna carried out by varying 
one parameter and keeping other parameters constant. The 
simulated return loss of the proposed antenna is shown in 
Fig. 2, which clearly indicates that the impedance bandwidth 
of the antenna is 8.3 GHz (3.1GHz -11.4 GHz) for return loss 
(Sn ) < -10 dB ( VSWR < 2). The ultra wideband is due to 
multiple resonances introduced by the combination of the 
rectangular slot and the tuning stub. The resonant frequency 
and bandwidth are controlled by the size of the rectangular 
slot, antenna and tuning stub. Detailed parametric analysis has 
been carried out and the final optimal parameter values of the 
antenna are listed in the Table 1 . However to study the impact 
of the slot, the slot length 'L 2 ' and width 'W 2 ' are varied by 
keeping one of them as constant. The current distribution, gain 
and group delay are also studied. 



Fig. 1 Geometry of the proposed slot antenna and its notched design. 

The effective length of the slot width is found to be 
approximately 0.5 ^ eff for the notch in the frequency band at 
the center frequency of 5.5 GHz. The introduction of the slot 
in the ground plane causes destructive interference in the 
current distribution which is responsible for the elimination of 
the specific frequency band. 
The effective wavelength of the slot width is, 

C 6+1 



u eff 



"eff 



2 



(1) 



where %' is the centre frequency of the notch band. 



The structure of the basic UWB slot antenna and its 
notched design is shown in Fig 1. The antenna consists of 
rectangular slot with width 'Wy and length 'Li'. The tuning 
stub comprises a triangular patch with height 'H'. The distance 
between the tuning stub and feed line is 'd'. 'W 2 ' and 'L 2 ' are 
the width and length of slot in the ground plane. ' W and 'L' 
are the overall width and length of the antenna respectively. In 



-5 














CO 
"O-10 

c 

o 

E -20 

3 

2-25 
-30 












1" 














-V- 








1 


I- 












-35 



























3 4 5 6 7 8 9 10 11 12 

Frequency in GHz 

Fig. 2 Simulated return loss of the proposed UWB antenna. 



TABLE I. 



Optimal Parameter Values of the Antenna 



Parameter 


Description 


Optimal 
Value 


L 


Length of the antenna 


28 mm 


W 


Width of the antenna 


21 mm 


Li 


Length of the slot 


15 mm 


Wi 


Width of the slot 


16.8 mm 


u 


Length of the slot in the 
ground plane 


1.8 mm 


w 2 


Width of the slot in the 
ground plane 


16.2 mm 


d 


Feed gap distance 


1.6 mm 


H 


Height of the patch 


9.3 mm 



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(IJCSIS) International Journal of Computer Science and Information Security, 



The simulated VSWR, with and without slot in the ground 
plane is shown in Fig. 3 and it is inferred that the antenna with 
slot has a impedance bandwidth of 3-.1 GHz to 11.4 GHz for 
VSWR < 2 (Sn < -10 dB) with notch band from 5.1 GHz to 
5.9 GHz. 
12 



10 



tt 8 

5 
« 

> G 





: 




y — Jl — 

j II 

■ with slot 




without slot 

i " 

i l¥ i 

i.i.L.. 


1 


-I*'*! Ill \f 


1^ fwi^^^/ 



u 3 4 5 6 7 8 9 10 11 12 
Frequency in GHz 

Fig. 3 Simulated VSWR with and without 
slot in the ground plane. 

A. Effect of Slot in the Ground Plane 

The slot is introduced in the ground plane as shown in 
Fig. 1 to obtain the notch in the specified band. Fig. 4a shows 
the VSWR variation for different slot lengths 'L 2 ' (for a 
particular width). It is observed that only a very small 
variation in the notch band for 28% of length variation with 
respect to the ground plane. Also it is noticed that the shift in 
notch band is directly proportional to slot length. Fig. 4b 
depicts VSWR variation for different widths' W 2 ' (L 2 =1.8 mm 
optimal value). It is noticed that the notch frequency is shifted 
towards higher frequency when slot width is decreased and 
vice versa. The rate of notch frequency shift per unit width is 
0.36 GHz. The optimal width value is 16.2 mm. 
12 



10 



OS 8 

i 

> 6 



2 \ 




L 2 = 0,5 mm 

L 2= 1 mm 

L 2 = 1-8 mm 

L* = 3 mm 




4 5 6 7 8 9 10 
Frequency in GHz 

(a) 



11 12 



12 
10 

ti 8 

> G 

4 

2 



Vol. 8, No. 3, 2010 




w 2 = 14mm 
W 2 = 15 mm 
w 2 = 16.2 mm 
w 2 = 17 mm 




u 3 4 5 6 7 8 9 10 11 12 
Frequency in GHz 

(b) 

Fig.3 Simulated VSWR a) differnet slot lengths'L 2 ' b) different slot 

widths 'W 2 ' 

B. Simulated Current Distribuition 

The simulated current distribution at frequency 5.5 GHz 
with and without slot in the ground plane is presented in Fig. 4. 
The current distribution of the proposed antenna is obtained by 
accounting the optimal design parameter values. In Fig. 4a the 
current distribution is mainly around the rectangular radiating 
slot and in the tuning stub. From the Fig. 4b, it is witnessed that 
the current distribution around the radiation slot is disturbed by 
the introduction of slot in the ground plane, which is 
responsible for the notch in the frequency band. 





(a) (b) 

Fig. 4 Current distributions at 5.5 GHz. a) without slot (b) with slot in the 
ground plane. 

C. Simulated Gain 

The computed gain of the UWB antenna with and without 
slot in the ground plane is compared in Fig. 5. It is observed 
that there is a gain variation at the notched frequency which is 
0.5 dBi where as it is 3.5 dBi for the antenna without slot and 
the gain varies from 2 dBi to 6 dBi across the UWB spectrum. 



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ISSN 1947-5500 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 



i 5 
= 4 

C 

"5, 











:::::: 


I ** 
1 * * 
1 * * 
■#■*---■- 
I* * 
I* 




i rs\ 




i J » i ~^i 
■ f + v 

i i j/j..i„„3sj 


L 




:__/«_*; _>!' L..V- 




| : ] **f —without slot 

■ ...with slot 

i i i i i i i 



6 7 8 9 
Frequency in GHz 



10 11 12 



Fig. 5. Comparison of simulated gain responses. 

D. Group delay per fromance 

The group delay 'x' of the antenna is calculated from the phase 
of the computed 'S 2 i' by using the following equation and 
plotted in Fig. 6, 



d(f) 
T = - 

df 



(2) 



where ' (j) ' is phase of S 2 i in radians /sec and T is frequency 
in GHz. 



I 


i i i 


1 


A 




*L ! 1 
*" , 1" \ 


ll * 

I: ■ *• 

III "3 










i i i 


i * 

-without si 

> with slot 

i 


nt --- 


i 


i i i 


Ol 



Si 

CO 


s« 

z 

£ 

■0-2 

Q. 
3 

2-3 

o 



3 4 5 6 7 8 9 10 11 12 
Frequency in GHz 

Fig. 6. Computed group delay. 

From the Fig. 6, it is noticed that the variation in the group 
delay for the antenna with and without slot is around 2 ns for 
the frequency range from 3.1 GHz to 10.6 GHz, but there is 
small variation in the group delay at the notch band in the 
response for the antenna with slot which is acceptable one. 

IV. EXPERIMENTAL!, RESULTS AND DISCUSSION 

The prototype of the proposed antenna shown in Fig. 1 was 
fabricated for different parameters with their optimal values 
and tested, which is depicted in Fig. 7. Using Hewlett Packard 
Network Analyzer (HP8757D), the VSWR is measured and 
compared with the simulation result is shown in Fig. 8. 




Fig. 7 Fabricated UWB slot antenna and its notch antenna. 

There is a discrepancy between the measured and the 
simulated ones might be the effect of soldering the SMA 
connector or fabrication tolerance. The simulation result was 
obtained by assuming coplanar as input port, whereas 
practically SMA connector was used, the imperfect transition 
between SMA feed to coplanar may introduce losses [20] and 
also the capacitances can lead to shift in the frequency. 



12 



10 



co6 



I I 


i i i i 


i 




: 




; ; 


l I I ! I 








jred -i *- 

ited ; :** 


* ' M 


t | Simuk 


\^^irf 

i i 


i i i i 


i 



11 12 



4 5 6 7 8 9 10 

Frequency in GHz 

Fig. 8. Comparison of measured and simulated 
VSWR. 



V. Time Domain Analysis 

In ultra wideband systems, the information is transmitted 
using short pulses. Hence it is important to study the temporal 
behavior of the transmitted pulse. The communication system 
for UWB pulse transmission must provide as minimum as 
possible distortion, spreading and disturbance. The channel is 
assumed to be linear time invariant (LTI) system to verify the 
capability of the proposed antenna for transmission and 
reception of these narrow pulses. The transfer function of the 
entire system is computed using simulated value of 'S 2 i' 
parameter [21]. The received output pulse is obtained by 
taking the Inverse Fourier Transform (IFT) of the product of 
transfer function and spectrum of the test input pulse. While 
computing 'S 2 i', two identical antennas are placed face to face 
position at a distance of 70 cm that is greater than the far- field 
distance of the antenna. 



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■5 



Q -10 
CO 

£-20 



i. 



e 
Z 



25 



-30 



■35 



-40 



J 


^ [ 

i. « 

M 


,*»■*[*■*-■-■*-■-■£ 


p^^^™.«.^v 




j j 






^ \ ■ ' 




y : ■ 






:..; ; 


^^~ Spectrum of the Input pulse 




FCC Indoor Emission Mask 



4 6 8 

Frequency in GHz 



10 



12 



Fig. 9. FCC mask with the spectrum of the test input pulse. 

The cosine modulated Gaussian pulse is considered for this 
analysis with centre frequency of 6.85 GHz and pulse width of 
220 picoseconds, whose spectrum is shown in Fig. 9. It 
satisfies the requirement of FCC mask for UWB indoor 
emission. The comparison of input and output received pulse 
for the antenna with notch and without notch is shown in 
Fig. 10, which ensures the distortion less pulse transmission 
and also guarantees that the designed antenna is capable of 
transmitting and receiving short pulses. The ringing effect in 
the waveform may be due to the transmission properties of the 
system. 



Of 

4) 0.: 









-0.5 



tysMp4 



DZ 




^^^ Input pulse 

— — - Received pulse from UWB antenna 

' Received pulse from UWB notch antenna 



12 3 4 

Time in nanosec. 

Fig. 10. Comparison of the received pulses from the UWB antenna without 
and with notch with input pulse. 

VI. Conclusion 

In this paper, a CPW fed UWB slot antenna with novel band 
notched design is proposed. The triangular stub is introduced 
at the anterior portion of the feed to enhance the coupling 
between the slot and feed. By embedding a slot in the ground 
plane, which is nearer to the radiating slot is responsible for the 
notch in the undesired band. With the above structural features 
the overall dimension of the proposed antenna comes around 
28x21x1.6 mm 3 . The antenna covers the entire UWB system 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3,2010 
ranging from 3.1 GHz to 11.4 GHz with band notching 
between 5.1 GHz and 5.9 GHz. Time domain analysis of the 
proposed antenna ensures the suitability of the antenna for 
UWB applications. Hence, this antenna configuration would be 
quiet useful for UWB indoor applications with no interference 
from WLAN and HYPERLAN/2 systems when they coexist. 

References 



[I] FCC NEWS(FCC 02-48), Feb. 14,2002. FCC News release, New 
public safety applications and broadband internet access among uses 
envisioned by FCC authorization of ultra wideband technology. 

[2] M. Ghavami, L.B. Michael and R. Kohno " Ultra Wideband Signals 

and Systems in Communication Engineering", John Wiley and sons. 
Inc., NY, USA, 2004. 

[3] K.L. Wong, " Compact and Broadband Microstrip Antenna", John 

Wiley and sons. Inc., NY, USA, 2001. 

[4] I. -J. Yoon, H. Kim et al, "Ultra- wideband tapered slot antenna with 

band cutoff characteristic," Electronics Letters, vol. 41, no. ll,pp. 
629-630, May 2005. 

[5] A. M. Abbosh, M. E. Bialkowski et al, " A Planar UWB antenna 

With Signal Rejection Capability in the 4-6 GHz Band," IEEE 
Microwave and Wireless Comp. Letters, vol. 16, no. 5, pp. 278-280, 
May 2006. 

[6] Keyvan Bahadori and Yahya Rahmat-Samii, "Miniaturized 

Elliptic-Card UWB antenna with WLAN Band Rejection 
for Wireless Communications," IEEE Trans. Antennas and 
Propag., vol.55, no. 11. pp.3326-3332,Nov. 2007. 

[7] Chong-Yu Hong, Ching-Wei Ling et al, " Design of a Planar 

Ultra- wideband Antenna With a New Band-Notch Structure ," 
IEEE Trans. Antennas and Propag. vol.55, no. 12, pp.339 1- 
3397,Dec. 2007. 

[8] Q.-X. Chu and Y.-Y. Yang ," 3.5 / 5.5 GHz dual band-notch 

ultra- wide band antenna," Electronics Letters, vol. 44, no. 3, pp. 
172-174, Jan. 2008. 

[9] L. Wang, . Wu, X.-W. Shi et al , "Design Of A Novel monopole 

UWB Antenna with a notched ground," PIER, vol. 5,pp. 13-20, 
2008. 

[10] Jia-Yi Sze and Jen-Yi Shiu, " Design of Band-Notched 

Ultrawideband square aperture antenna with a hat-Shaped Back- 
Patch," IEEE Trans. Antennas and Propag., vol.56, no. 10. pp. 3391- 
3397,Oct. 2008. 

[II] Yi-Cheng Lin and Kuan- Jung Hung, "Compact Ultrawideband 
Rectangular Aperture Antenna and Band-Notched Designs," IEEE 
Trans. Antennas Propag, vol.54, no.l 1, pp.3075-3081, Nov.2006. 

[12] Hyung Kuk Yoon, Yohan Lim et al, " UWB Wide Slot antenna 

with Band -notch Function," Proc. IEEE Antennas and Propag. 

society Intl Symposium, pp. 3059-3062, July 2006. 
[13] Wen-jun Lui, Chong-hu Cheng and Hong-bo Zhu, "Improved 

Frequency Notched Ultra wideband Slot Antenna Using Square 

Ring Resonator," IEEE Trans. Antennas Propag., vol.55, no. 9, 

pp.2445-2450, Sept.2007. 
[14] Yunlong Cai and Zhenghe Feng , " A UWB Antenna with 

novel L branches on ground for Band-Notching Application ," 

Proc. Of IEEE Intl. Conference on Microwave and Millimeter 

wave Tec, vol. 4, pp.1654-1657, April 2008. 
[15] The-Nan Chang and Min-Chi Wu , " Band-Notched Design for 

UWB Antennas , " IEEE Antennas Wirel. Propag. Letters, vol.7, 

pp.636- 639, 2008. 
[16] C- Y. Huang, S.- A. Huang and C- F. Yang , " Band-notched 

ultra-wideband circular slot antenna with Inverted C-shaped 

parasitic strip,'" Electronics Letters , vol. 44, no. 15, pp. 891-892, July 

2008. 
[17] IE3D 14, Zeland Software, Ins., Fremont, USA. 

[18] C. Balanis ,Antenna Theory Analysis and Design, 3rd edition, New 

York , Wiley, 2005 
[19] J. William and R. Nakkeeran , "A CPW-fed wideband slot antenna 

with triangular patch," Proc. of IEEE Intl. Conference on Computing, 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3,2010 

Communication and Networking , Dec. 2008. DOI. current research interest is in the area of coplanar waveguide feed antennas 

10. 1 109ICCCNET.2008.4787770. and printed slot antennas for UWB. 
[20] Kuang-ping ma, Yongxi Qian and Tatsuo Itoh, "Analysis and 

applications of a New CPW- slot line Transition," IEEE R. Nakkeeran Received BSc. Degree in Science and B.E degree in 

Transactions on Microwave theory and Techniques, vol. 47, pp. Electronics and Communication Engineering from the Madras University in 

426-432, April 1999. 1987 and 1991 respectively and M.E degree in Electronics and 

[21] Stanislas Licul and William A Davis, " Ultra wide band (UWB) Communication Engineering (diversification in Optical Communication) from 

antenna measurements using vector Network analyzer," IEEE the Anna University in 1995. He received Ph.D degree from Pondicherry 

Antennas and Propagation International Symposium, pp. 1319- University in 2004. Since 1991, he has been working in the teaching 

1322, June 2004. profession. Presently, he is Assistant Professor in Pondicherry Engineering 

AUTHORS PROFILE College. He is life member of IETE, ISTE, OSI and IE(I). Also he is member 

T „ 7 .„. ■ i i ■ -r» t- i • T-i • i^ • .• ^ of OS A, SPIE and IEEE. He has published seventy five papers in National and 

J.Wilham received his B.E. degree in Electronics and Communication from T . .. , ~ r „ ,. , T , T T , ,, , 

„, Al . , TT • ■._ -T, ., i T i- i .i ^^^11 • International Conference Proceedings and Journals. He has co-authored a 

Bharathidasan University, Tamilnadu, India, and the M. Tech. degree in , , ,,• , j , mn TT - r ■ *. , ^ ±- i ^ ■ A - 

„ . x . „ A J ' XT A . ' T ' A „ „ . , ^ TTT , book, published by PHI. His areas of interest are Optical Communication, 
Communication Systems irom National Institute ol Technology (N.I.T), 

Trichy,India, in 1991 and 2006 respectively. He is currently working towards Networks, Antennas, Electromagnetic Fields and Wireless Communication, 
the Ph.D. degree at Pondicherry Engineering College, Pondicherry, India. He 
is a life member of ISTE and IE (I) and member of IEICE and EurApp. His 



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A STUDY OF VARIOUS LOAD BALANCING TECHNIQUES IN 

INTERNET 

M.Azath 1 , Dr.R.S.D.Wahidabanu 2 , 

Research Scholar, Anna University, Coimbatore. 

1 mailmeazath @ gmail.com 

2 Research Supervisor, Anna University, Coimbatore. 

2 drwahidabanu @ gmail.com 



Abstract 

One of the most important applications of 
traffic engineering is load balancing. 
Successful implementation of load balancing 
depends on the underlying routing protocol 
that provides connectivity through the 
Internet by determining the routes used by 
traffic flows. But the load-balancing 
problem is not yet solved completely; new 
applications and architectures are required to 
meet the existing or incoming fastest 
Internet world. And, for greatest impact, 
these new capabilities must be delivered in 
toolkits that are robust, easy-to-use, and 
applicable to a wide range of applications. 
For balancing traffic in internet, packets 
should be reorder, reordering also having a 
problem for flows in internet. In Internet, 
unresponsive flows easily occupy the 
limited buffers, there by reducing the 
Quality of Service (QoS). In this paper, 
various techniques that are adopted for load 
balancing in Internet are analyzed. 

Keywords: 

Traffic engineering, Load Balancing, 
Internet Services, unresponsive flows, QOS, 
Buffer, Traffic splitting and Router. 

1. Introduction 



traffic has to be carried in the network in 
such a way that performance objectives are 
fulfilled. In computer networking, load 
balancing is a technique to spread work 
between two or more computers, network 
links, CPUs, hard drives, or other resources, 
in order to get optimal resource utilization, 
throughput, or response time. 

One of the most common applications of 
load balancing is to provide a single Internet 
service from multiple servers, sometimes 
known as a server farm. Commonly load 
balanced systems include popular web sites, 
large Internet Relay Chat networks, high 
bandwidth File Transfer Protocol sites, 
NNTP servers and DNS servers. 

The idea of load balancing is to move traffic 
from congested links to other parts of the 
network in a well-controlled way. Traffic 
engineering seeks to effectively balance 
traffic load throughout existing networks, 
thus achieving QoS demands and 
minimizing typical costs of adding hardware 
and software implementations, common to 
network engineering. When dealing with 
real-world cases of load balancing, both 
network and traffic engineering is general 
purpose tools used throughout all steps of an 
implementation [12]. 



Traffic engineering refers to the 
performance optimization of operational 
networks. On one hand, traffic offered 
between origin and destination nodes loads 
the network and on the other hand, this 



2. Motivation 

Load balancers are an integral part of today's 
Web infrastructure. They're also complex 
and under-documented pieces of hardware. 



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Today's Web sites are complex beasts. 
Every component must work together to 
create a site that is greater than the sum of 
its parts. 



site. This is why a firewall is often a suspect, 
too, but to a lesser degree since it is 
generally a simpler device than load 
balancers [1]. 



^ Internet 




Routers 












Firewalls 










1 Load Balancers 










1 Web Servers 










1 App Servers 










DB Servers 









Figure 1 : Traffic flow for a load balancer 

The Internet is connected to the routers, 
which pass traffic through a firewall to the 
load balancers, which distribute the traffic to 
the Web servers, which pass information to 
the application server bone, and the 
application server bone is connected to the 
database server bone. We get the picture. If 
one component or piece of the process fails, 
it can take down the entire site. 

Load balancers are also in the direct path of 
all traffic to a particular Web site. By 
looking at Figure 2 below, we can see that if 
the load balancer stops working, the entire 
site stops working. This critical position in 
the infrastructure can make it appear as 
though the load balancer is the problem, 
even in cases where it is not (such as a 
firewall issue, a back-end database problem, 
someone tripping over a cable, etc.). Unlike 
a broken or malfunctioning Web server, a 
miss configured or malfunctioning load 
balancer will result in a dead-to-the-world 




Internet Users 



Web Server 1 



I WebS< 




Figure 2: Load Balancer implementation 

3. Needs of load balancing 

Load balancing is common in ISP networks. 
If the traffic demands are known, the load 
balancing can be formulated as an 
optimization problem. However, knowledge 
of traffic demands is often lacking. Recent 
trends in load balancing points toward 
dynamic protocols [13]. These protocols 
map the traffic of router onto multiple paths 
and adapt the share of each path in real-time 
to avoid hot-spots and cope with failures. 
Dynamic load balancing needs schemes that 
split traffic across multiple paths at a fine 
granularity. 

Since the power of any server is finite, a 
web application must be able to run on 
multiple servers to accept an ever increasing 
number of users. This is called scaling. 
Scalability is not really a problem for 
intranet applications since the number of 
users has little chances to increase. 

However, on internet portals, the load 
continuously increases with the availability 
of broadband Internet accesses. The site's 
maintainer has to find ways to spread the 



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load on several servers, either via internal 
mechanisms included in the application 
server, via external components, or via 
architectural redesign. 

Further complication increases due to 
various factors such as: 

• Sizes of objects might not be the 
same. 

• Object IDs might not be chosen at 
random. 

• Heterogeneity in the capabilities of 
nodes. 

4. Types of load balancing and its 
approaches 

Load balancing algorithms can be classified 
into three main classes: static algorithms, 
dynamic algorithms, and adaptive 
algorithms. Static algorithms decide how to 
distribute the workload according a prior 
knowledge of the problem and the system 
characteristics. Dynamic algorithms use 
state information to make decisions during 
program execution. Finally, Adaptive 
algorithms are a special case of dynamic 
algorithms. They dynamically change its 
parameters in order to adapt its behavior to 
the load balancing requirements [10]. 

As though there are three main algorithms 
for load balancing, there are different load 
balancing approaches available. They are: 

1. Client-side load balancing: Client-side 
load balancing is not a normal practice, but 
it is indeed possible. For some times, 
Netscape incorporated a simple balancing 
algorithm in their Navigator browser, 
making it choose a random Netscape web- 
server when visiting www.netscape.com. 
[2]. 

2. Core Internet routing uses protocols and 
agreements that allow for automated load 
balancing and fail-over mechanisms. These 



are commonly based on the Border Gateway 
Protocol, used for data-exchange between 
large Internet operators. 

3. DNS -based load balancing is a popular 
way of distributing traffic amongst a set of 
Internet addresses by returning a list of 
active addresses to the requesting client. 
These addresses can point to a set of servers 
or even a set of geographically separate 
sites. 

4. Sites can connect to the net through 
several links, a practice known as multi- 
homing. This enables both incoming and 
outgoing load balancing, in addition to the 
increased redundancy. 

5. Dispatcher-based load balancing is used 
within a site to balance load between a set of 
real servers. Generally, the dispatcher 
assumes a virtual address for a service and 
receives requests which it then redirects to 
an appropriate server based on given 
criteria. 

6. The real servers can again operate some 
form of balancing mechanism to decide 
whether to handle the request or redirect it to 
a more suitable server or site. 

7. Content servers could access back-end 
servers - typically running databases and 
low-level services - in a load-balanced 
fashion. 

8. Back-end servers could also incorporate 
balancing amongst themselves to avoid 
over-utilization. 

When talking about varying levels of load 
balancing, it is fair to identify the level to be 
proportional to the distance from the content 
served - long distance equals high level, and 
vice versa. In an informal manner, we can 
designate steps 1 through 4 in the figure as 
high-levels, and steps 5 through 8 as low- 
levels of load balancing [12]. 

5. Adopted techniques 

There are various papers which explain 
about the load balancing in Internet. Each of 



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them is routed in different directions and 
with various considerations. 

Router mechanisms designed to achieve fair 
bandwidth allocations, like Fair Queueing, 
have many desirable properties for 
congestion control in the Internet. In [6], 
architecture is proposed that significantly 
reduces this implementation complexity yet 
still achieves approximately fair bandwidth 
allocations. This architecture has two key 
aspects: 

1. To avoid maintaining per flow state at 
each router, we use a distributed algorithm 
in which only edge routers maintain per 
flow state, while core (non-edge) routers do 
not maintain per flow state but instead 
utilize the per-flow information carried via a 
label in each packet's header. This label 
contains an estimate of the flow's rate; it is 
initialized by the edge router based on per- 
flow information, and then updated at each 
router along the path based only on 
aggregate information at that router. 

2. To avoid per flow buffering and 
scheduling, as required by Fair Queueing, 
we use FIFO queueing with probabilistic 
dropping on input. The probability of 
dropping a packet as it arrives to the queue 
is a function of the rate estimate carried in 
the label and of the fair share rate at that 
router, which is estimated based on 
measurements of the aggregate traffic. The 
limitation here is in the destination, packet 
reorganizing should be there. 



comparable performance to 16-bit CRC. As 
though hashing methods provide best 
performance, new hash based algorithms are 
needed that have less computational 
complexity. 



In [7], they proposed a per-class queue 
management and adaptive packet drop 
mechanism in the routers for Internet 
congestion control. An active queue 
management is modeled as an optimization 
problem and the proposed mechanism 
provides congestion control and fairness for 
different types of traffic flows. An optimal 
packet drop rate is obtained to maintain a 
relatively small queue occupancy, which 
provides a less queue delay delivery of 
packets. Moreover, the queue occupancy 
and the packet drop rates obtained are both 
upper bounded, which is meaningful for 
providing the class-based guaranteed delay 
services for real-time multimedia 
applications. They model the general AQM 
as an optimization problem, and try to obtain 
a minimal packet drop rate that results in 
low queue occupancy. Compared with RED 
that controls the average queuing delay in 
the router, the per-class queue management 
and optimal packet drop mechanism can 
obtain the minimal queuing delay and hence 
the end-to-end delay. The major drawback 
in this paper is that the packet drop rate is 
minimal and the resulting queue occupancy 
is also kept minimal. 



In [18], they evaluated 5 direct hashing 
methods and one table-based hashing 
method. While hashing schemes for load 
balancing have been proposed in the past, 
this is the first comprehensive study of 
performance using real traffic traces. They 
find that hashing using 16-bit CRC over 
TCP five tuples gives excellent load 
balancing performance. Load-adaptive table- 
based hashing uses the exclusive-OR of the 
source and destination IP addresses achieve 



In [19], a novel packet scheduler is proposed 
and is called Stratified Round Robin, which 
has low complexity, and is amenable to a 
simple hardware implementation. In 
particular, it provides a single packet delay 
bound that is independent of the number of 
flows. This property is unique to Stratified 
Round Robin among all other schedulers of 
comparable complexity. 



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An important component of the many QoS 
architectures proposed is the packet 
scheduling algorithm used by routers in the 
network. The packet scheduler determines 
the order in which packets of various 
independent flows are forwarded on a shared 
output link. One of the simplest algorithms 
is First Come First Served (FCFS), in which 
the order of arrival of packets also 
determines the order in which they are 
forwarded over the output link. FCFS 
clearly cannot enforce QoS guarantees, as it 
allows rogue flows to capture an arbitrary 
fraction of the output bandwidth. 

Stratified Round Robin operates as a two- 
step scheduler: 

1. The first step uses the flow class 
mechanism to assign slots to each 
flow fi in proportion to its 
approximate weight as defined by 
the flow class Fk to which it belongs. 

2. The second step uses the weight- 
proportional credit mechanism to 
ensure that each flow fi receives 
service in proportion to its actual 
weight wi. 

The advantage of this approach is that it 
considerably simplifies the scheduling 
decision to be made. But we are having the 
difficulty of considerable packet 
reorganizing in the destination. 

In this paper [11], based on measurements of 
Internet traffic, they examined the sources of 
load imbalance in hash-based scheduling 
schemes. They proved that under certain 
Zipf-like flow-size distributions, hashing 
alone is not able to balance workload. They 
introduced a new metric to quantify the 
effects of adaptive load balancing on overall 
forwarding performance. To achieve both 
load balancing and efficient system resource 
utilization, they proposed a scheduling 
scheme that classifies Internet flows into 



two categories: the aggressive and the 
normal, and applies different scheduling 
policies to the two classes of flows. They 
have stated that their work is unique in 
exploiting flow-level Internet traffic 
characteristics. 

In [13], a new mechanism called Flow-let 
Aware Routing Engine (FLARE) is 
proposed in which a new traffic splitting 
algorithm that operates on bursts of packets, 
carefully chosen to avoid reordering. Using 
a combination of analysis and trace-driven 
simulations, it is shown that FLARE attains 
accuracy and responsiveness comparable to 
packet switching without reordering packets. 
FLARE is simple and can be implemented 
with a few KB of router state. Highly 
accurate traffic splitting can be implemented 
with little to no impact on TCP packet 
reordering and with negligible state 
overhead. Owlets can be used to make load 
balancing more responsive, and thus help 
enable a new generation of real-time 
adaptive traffic engineering. 

Static load balancing is presented in [3]. The 
static problem is possible to be formulated 
and solved only if we have precise 
information on all the traffic demands. 
However, such information is not available 
or traffic condition change unexpectedly. In 
dynamic load balancing, dynamically 
changing network status information are 
utilized [5],[8],[14],[4],[15]. 

Traffic Engineering (TE) of dynamic 
methodologies is classified into two basic 
types: time-dependent and state-dependent. 
In time-dependent TE, traffic control 
algorithms are used to optimize network 
resource utilization in response to long time 
scale traffic variations. In state-dependent 
TE, traffic control algorithms adapt to 
relatively fast network state changes. State- 
dependent load balancing is a key technique 
for improving the performance and 



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scalability of the Internet. The fundamental 
problem in dynamic load balancing in 
distributed nodes involves moving load 
between nodes. Each node can transfer load 
to at most one neighbor also, any amount of 
load can be moved along a communication 
link between two nodes in one step. The 
dynamic load balancing is formed with 
incomplete information. More precisely, it is 
assumed that the traffic demands are 
unknown but the link loads are periodically 
measured using a measurement system as in 
[9], [16] [17]. 

Floyd et at. [20] study congestion collapse 
from undelivered packets. This situation 
arises when bandwidth is continuously 
consumed by packets at the upstream that 
are dropped at the downstream. Several 
ways to detect unresponsive flows are 
presented. It is suggested that routers can 
monitor flows to detect whether flow is 
responsive to congestion or not. If a flow is 
not responsive to congestion, it can be 
penalized by discarding packets to a higher 
rate at the router. According to the authors 
there are some limitations of these tests to 
identify non-'TCP-friendly flow". It does 
not help to save bandwidth at the upstream if 
the flow sees the congestion at the 
downstream because this solution does not 
propagate the congestion information from 
downstream to upstream [21]. 

In [22], architecture which contains an 
adaptive packet scheduler with a bursty 
traffic splitting algorithm is proposed for 
better load balancing. The scheduler has a 
classifier which classifies the flows into 
aggressive and normal flows. Aggressive 
flows are treated as high priority flows. 
Based on the buffer occupancy threshold, a 
trigger handler checks for load un-balance of 
the network and automatically triggers the 
load adapter. The load adapter reroutes the 
high-priority aggressive flows into the least 
loaded best path, using the bursty traffic 



splitter algorithm. As though it is adaptive 
algorithm for load balancing, it does not 
satisfy the needs when we are coming across 
the unresponsive flows. 

6. Conclusion 

Static load balancing is suffering from lack 
of all the available information while we 
perform for load balancing. Similarly 
dynamic load balancing needs schemes that 
split traffic across multiple paths at a fine 
granularity. In adaptive load balancing the 
performance of the network can be 
improved by parameter adjustments of the 
routes, traffic splitting, and scheduling. Thus 
in this current fast Internet scenario, a new 
adaptive load balancing algorithm is needed. 
The adaptive load balancing is also not 
sufficient for some situations like 
unresponsive flows of traffics. Hence there 
is a need that for load balancing algorithm 
with the additional feature of detecting 
unresponsive flows of the incoming traffic 
in Internet. 

References 

[1] 

http : //www . oreillynet . com/pub/a/oreilly/net 

working/news/slb 0301.html Feb 2010. 

[2] Dan Mosedale, William Foss, and Rob 
McCool, "Lessons learned administering 
netscape's internet site." IEEE Internet 
Computing, l(2):28-35, 1997. 

[3] Grenville Armitage, 2000. MPLS the 
magic behind the myths. IEEE Commun. 
Mag., 38: 124-131. 

[4] Dinan, E., D. Awduche and B. Jabbari, 
"Analytical framework for dynamic traffic 
partitioning in MPLS networks", IEEE 
International Conference on 

Communications, (ICC 00), 18-22 June 
2000, New Orleans, Volume-3, pp: 1604- 



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1608. 

[5] Elwalid, A., C. Jin, S. Low and 
I.Widjaja, MATE: MPLS adaptive traffic 
engineering. IEEE Infocom., Twentieth 
Annual Joint Conference of the IEEE 
Computer and Communications Societies, 
Volume-3, April 22-26 2001, Anchorage, 
Alaska, USA. ISBN 0-7803-7016-3, 1300- 
1309. 

[6] Core-Stateless Fair Queuing: A Scalable 
architecture to approximate fair bandwidth 
allocations in high speed networks, Ion 
Stoica, Scott Shenker, Hui Zhang, 2003. 

[7] Mei-Ling Shyu, Shu-Ching Chen, 
Hongli Luo, "Per-class queue management 
and adaptive packet drop mechanism for 
multimedia networking", Proceedings of the 
2003 International Conference on 
Multimedia and Expo - Volume 3 (ICME 
'03) -Volume 03. 

[8] Song, J., S. Kim, M. Lee, H. Lee and T. 
Suda, "Adaptive load distribution over 
multipath in MPLS networks". IEEE 
International Conference on 

Communications (ICC ' 03 ) , Anchorage , 
Alaska, Date: 11-15 May 2003 pp: 233-237. 

[9] Butenweg, S., "Two distributed reactive 
MPLS traffic engineering mechanism for 
throughput opt'imization in Best effort 
MPLS networks, In the Eighth IEEE 
International Symposium on Computers and 
Communications (ISCC'03). 379-384 vol.1, 
30th June-3rd July 2003. 

[10] Mohammed Aldasht, Julio Ortega, 
Carlos G. Puntonet; Antonio F. Diaz, "A 
Genetic Exploration of Dynamic Load 
Balancing Algorithms", IEEE 2004. 



[11] Shi, W. 
Gburzynski, P. 



Macgregor, M.H. 
'Load Balancing For 



Parallel Forwarding" Networking, Ieee/Acm 
Transactions On , Aug. 2005. 

[12] Sven Ingebrigt Ulland, "High level load 
balancing for web services", University of 
Oslo, 20 th May 2006. 

[13] Srikanth Kandula, Dina Katabi, 
Shantanu Sinha, Arthur Berger "Dynamic 
Load Balancing Without Packet Reordering" 
Acm Sigcomm Computer Communication 
Review ,Volume 37 , Issue 2 (April 2007). 

[14] Murugesan, G. and A.M. Natarajan, 
"Adaptive granularity algorithm for 
effective distributed load balancing and 
implementation in multiprotocol label 
switching networks". IEEE, International 
Conference on Advanced Computing and 
Communication (ADCOM'07) 18-21 
December 2007, pp: 626-631. 

[15] Dengyin Zhang, Zhiyun Tang and 
Ruchuan Wang, "Automatic traffic balance 
algorithm based on traffic engineering". J. 
Network Syst. Manage., 14: 317-325. 

[16] Ashwin Sridharan, Roch Guerin and 
Christophe Diot, "Achieving near-optimal 
traffic engineering solution for current 
OSPF/IS-IS networks". In the IEEE/ACM 
Trans. Network., 13: 234-247. 

[17] G. Murugesan, A.M. Natarajan and C. 
Venkatesh, "Enhanced Variable Splitting 
Ratio Algorithm for Effective Load 
Balancing in MPLS Networks" Journal of 
Computer Science 4 (3): 232-238, 2008 
ISSN 1549-3636 2008 Science Publications. 

[18] Zhiruo Cao, Zheng Wang, Ellen 
Zegura, "Hashing-based traffic splitting 
algorithms for Internet load balancing", 
Georgia Institute of Technology 1999. 

[19] S. Ramabhadran and J. Pasquale, 
"Stratified Round Robin: A Low 



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Complexity Packet Scheduler with 
Bandwidth Fairness and Bounded Delay," 
Proc. Acm Communications Architectures 
and Protocols Conf. (Sigcomm), Karlsruhe, 
Germany, Pp. 239-249, Aug. 2003. 

[20] S. Floyd and K. Fall. Promoting the use 
of end-to-end congestion control in the 
Internet. IEEE/ ACM Transactions on 
Networking, Aug. 1999. 

[21] Ahsan Habib, Bharat Bhargava, 
"Network Tomography -based Unresponsive 
Flow Detection and Control", Department of 
Computer science, Purdue University, IN 
47907-1398. 

[22] M.Azath, Dr.R.S.D.Wahida banu, 
"Load balancing in Internet Using Adaptive 
Packet Scheduling and Bursty Traffic 
Splitting", International Journal of 
Computer Science and Network Security, 
Vol.8, No. 10, Oct 2008. 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 



Laboratory Study of Leakage Current and 

Measurement of ESDD of Equivalent Insulator Flat 

Model under Various Polluted Conditions 



N. Narmadhai 

Senior Lecturer, Dept of EEE 

Government College of Technology 

Coimbatore, India 

narmadhai @ get. ac. in 



S. Suresh 

PG Scholar, Dept of EEE 

Government College of Technology 

Coimbatore, India 

suresh. sundararaju® gmail.com 



Dr. A.Ebenezer Jeyakumar 

Director (Academics) 

SNR Sons Charitable Trust, SREC 

Coimbatore, India 

ebeyjkumar® rediffmail.com 



Abstract — The phenomenon of flashover in polluted insulators 
has been continued by the study of the characteristics of 
contaminating layers deposited on the surface of insulators in 
high voltage laboratories. This paper proposed the Equivalent 
insulator flat plate model for studying the flashover phenomena 
due to pollution under wet conditions even at low voltage. 
Laboratory based tests were carried out on the model under AC 
voltage at different pollution levels. Different concentrations of 
salt solution has been prepared using sodium chloride, Kaolin 
and distilled water representing the various contaminations. 
Leakage current during the experimental studies were measured. 
A conductivity measuring instrument (EQ-660 A) is used to 
measure the conductivity of the salt-solution. Salinity and 
Equivalent salt Deposit Density (ESDD) were calculated. Test 
results in terms of conductivity and ESDD are plotted against salt 
concentration and the relationship between the conductivity and 
ESDD is examined. 

Reported results of this preliminary study on the insulator 
model simulates the distinctive stages of development of flashover 
due to the pollution and it could be easily identified from the 
contamination level of ESDD and from the magnitude of leakage 
current. 

Keywords-Conductivity, ESDD, Flashover, Insulator model, 
Leakage Current, Salt solution.) 

I. Introduction 

Insulators used in outdoor electric power transmission lines 
are exposed to outdoor environmental contaminations. 
Contamination on outdoor insulators enhances the chances of 
flashover. Depending on the nature and duration of exposure, 
deposits of wind-carried industrial, sea and dust contaminants 
build up on the insulator surface as a dry layer. The leakage 
current path through a layer of dry contaminants on an 
insulator surface is capacitive wherein the current amplitude is 
small and sinusoidal. The dry contaminant layer becomes 
conductive when exposed to light rain or morning dews. As 
wetting progresses, the leakage current path changes from 
capacitive to resistive with simultaneous increase in current 
amplitudes. The increase in leakage current dries the 
conducting layer and forms the dry bands around the areas 
with high current density. These dry bands interrupt the 
current flow and most of the applied voltages are impressed 



across these narrow dry bands. If the dry bands cannot 
withstand the voltage, localized arcing develops and the dry 
bands will be spanned by discharges. The arcs merge together 
and form a single arc, which triggers the surface flashover [1]. 

The contamination severity determines the frequency and 
intensity of arcing and, thus the probability of flashover. In 
favourable conditions when the level of contamination is low, 
layer resistance is high and arcing continues until the sun or 
wind dries the layer and stops the arcing. Continuous arcing is 
harmless for ceramic insulators. The mechanism described 
above shows that heavy contamination and wetting may cause 
insulator flashover and service interruptions. Contamination in 
dry conditions is harmless. B. F. Hampton investigated the 
voltage distribution along the wet, polluted surface of a flat 
insulating strip and the method of dry band formation, with 
subsequent growth of discharges on the polluted surface [2]. 
Verma measured the peak leakage current and correlated the 
current with the flashover voltage. He suggested that the 
flashover is imminent if the leakage current peak exceeds 
100mA [3]. Karady observes the same [4]. 

In practice, there are various contaminant types that settle 
on outdoor insulators. These contaminants can be classified as 
soluble and insoluble. Insulators located near coastal regions 
are typically contaminated by soluble contaminants, especially 
salt (or sodium chloride). Insulators located near cement or 
paper industries are typically contaminated by non- soluble 
contaminants such as calcium chloride, carbon and cement 
dust. Irrespective of the type of contaminant, flashover can 
occur as long as the salts in the contaminant are soluble 
enough to form a conducting layer on the insulator's surface. 
In order to quantify the contaminants on the surface of the 
insulators, the soluble contaminants are expressed in terms of 
Equivalent Salt Deposit Density (ESDD), which correlates to 
mg of NaCl per unit surface area. Non- soluble contaminants 
are expressed in terms of Non-Soluble Deposit Density 
(NSDD), which correlates to mg of kaolin per unit surface 
area. 

Many researchers studied that the leakage current due to the 
contamination level is the main cause for flashover. 
M.A.M.Piah and Ahmed Dams [5] modelled the leakage 



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current in terms of conductivity and other environmental 
parameters. I.R. Vazquez et al [6] have tested the non ceramic 
insulators by non standard method where they expressed the 
contamination level by ESDD. F. V. Topalis et al [7] studied 
the critical flashover voltage of the insulator with the variation 
of surface conductivity and ESDD. In Guan Zhicheng et al [11] 
the maximum value of leakage current has the definite 
relationship with the flashover voltage of the polluted insulator 
used to express the pollution degree of insulator. 

In this paper, flat plate model for studying the insulator 
flashover phenomena has been presented. Experimental tests 
have been conducted on the model under various polluted 
conditions. The study on the leakage current is important in 
order to diagnose the insulator condition by monitoring of the 
leakage current. Here both ESDD and surface conductivity for 
various quantities of contaminated salt deposits are measured 
using the IEC standard [8]. 

II. Flat Plate Model and Experimental Setup 

A. Equivalent Insulator Model 

The simplified geometrical models equivalent to actual 
insulator are being widely used for the purpose of flashover 
analysis. Among these models, the basic flat trough model has 
merited extensive attention in the context of pollution 
flashover. So the proposed model, equivalent to standard disc 
insulator made of an insulating glass material with two copper 
terminals, one on cap and another at the pin. A simplified plan 
of the insulator model is shown in Fig. 1 



(IJCSIS) International lournal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
current is shown in Fig 2. The slurry is poured so that it rolls 
off uniformly in the trough. 



JL 1 



6cm 



-IS. 5cm 



Fig. 1. Flat Trough Model 

B. Experimental Setup 

A proposed equivalent insulator trough model [1] of 
dimension 1 8. 5x 0.6x 0.2cm is used for the contamination 
flashover experiments. The principal application of this 
equivalent model would be to help simulate as much as 
possible the practical conditions of high voltage insulators in 
the application of low voltage itself. In artificial testing, a 
contaminant is usually substituted by a dissolved mixture of an 
inert binder-Kaolin and NaCl salt. The inert binder is 
supposedly non-conducting and the quantity of salt represents 
the level of contamination. Contamination salt solution was 
prepared for various NaCl values of 15g, 20g, 25g, and 30g. 
The mixture, usually dissolved in distilled water is known as 
slurry which is thoroughly mixed as per IEC standard [8]. 
Before coating, the trough is initially washed and wiped clean 
and dry. The experimental setup to measure the leakage 



© 



®. 



{■D-25-5ilA) 



Fig. 2. Experimental Setup 

A test voltage of 230V, 50 Hz was applied across the 
terminals and the leakage current is monitored through the 
suitable measuring meter from the instant of application of 
voltage till the formation of dry band. The dry band was 
precisely located on the model. Its shape, contour of growth 
and locations were physically measured. The test results either 
in a flashover or a withstand. The conductivity and ESDD has 
also been calculated from the deposited contaminations. The 
contamination can thus be classified as light, medium or heavy 
according to the IEC standard [9]. 

III.Esdd Calculation 

In any insulator severity of pollution is characterized by 
the Equivalent Salt Deposit Density (ESDD). The procedure 
for calculating the ESDD [8] is as follows: After the test has 
been completed, the deposits were collected by a small brush 
from the contaminated plate and mixed with 1 litre of distilled 
water to get the solution for specific area of the glass plate. 
This process is repeated for the other samples of salt solutions. 
The conductivity of each collected salt solution is measured 
using a conductivity meter which is initially calibrated using 
0.1N KC1 solution. At the same time temperature is also 
recorded. The conductivities at different temperature are 
converted to 20° temperatures by the expression [8] as, 



°m =ff fl [l-K0-2D)] 



(1) 



Where, 

6 is the solution temperature, °C 

o e is the volume conductivity at a temperature 6°C (S/m) 

o 2 o is the volume conductivity at a temperature 20°C (S/m) 

b is the factor depending on the temperature 6 as given in 

Table I. 

Table I values of b at different temperatures 



e°c 


b 


5 


0.03156 


10 


0.02817 


20 


0.02277 


30 


0.01905 



The salinity S a of the solution is determined by the following 
expression [8] as, 



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■S* = (S.7^d) L 



Finally, the equivalent salt deposit density can be determined 
by the following expression [8] as, 



ESDD = 



Where, 



s a xv 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 

(2) current caused the heating effect which leads to rapid dry band 
formation and partial discharges across these dry bands. Due 
to the higher resistance at pin and cap end the heat dissipated 
in that location may be greater and therefore moisture dried 
rapidly. After 10 minutes dry bands could not sustain the 

(3) applied voltage cause the scintillations to occur which 
ultimately leads to flashover. The scintillations have been 
physically seen and captured using high speed camera. 



V is the volume of the solution, cm 
A is the area of the cleaned surface, cm 2 



According to IEC 60815, pollution site severity classification 
are shown in Table II [9] 

Table II Pollution Site severity (IEEE definitions) 



Description 


ESDD 


Very Light 


0-0.03 


Light 


0.03-0.06 


Medium 


0.06-0.1 


Heavy 


>0.1 



IV. Artificial Contamination Test 

A. Light Contamination 

Insulators are mostly affected by flashover due to the 
deposition of NaCl salt particles. Therefore the equivalent 
model was uniformly sprayed with the slurry solution 
consisting of 15g NaCl, 40g Kaolin and 1 litre of distilled 
water. Leakage current started to flow on the surface of 
insulator due to the pollution. It is observed that there is an 
increase in leakage current magnitude when compared with 
clean surface condition, which is mainly because of increase in 
surface conductivity. Dry bands have started to form on the 
polluted surface after reaching the maximum leakage current 
of 42mA. The complete dried condition of pollution layer with 
complete dry band was physically seen after 60 minutes 
approximately. No flashover could be seen and the test results 
in a withstand. 

Similarly for 20g NaCl, the leakage current increased to 
90mA and the time taken for the formation of dry band is 
reduced compared to 15g.The test results in a Withstand. 

B. Medium Contamination 

Experiments were repeated for 25g NaCl. It is noticed that 
the magnitude of maximum leakage current increased to 
130mA. It is because that the current magnitude depends on 
the level of contamination and the amount of moisture on the 
insulator surface. The test results in withstand but the field 
exceeds the withstand capability and it initiates the arc 
discharge. 

C. Heavy Contamination 

Finally the insulator model is contaminated by 30g NaCl 
solution and experiments were repeated in a similar way. Due 
to the high contamination of NaCl the magnitude of leakage 
current goes upto 220mA. The high magnitude of leakage 



V. Results and Discussions 

A. ESDD 

The correction conductivity, salinity and ESDD have been 
calculated for various tests as per IEC-507. The measured 
Salinity, ESDD and conductivities for 15g, 20g, 25g and 30g 
of NaCl salt are shown in Table III. 

Table III Values of conductivity, Salinity & ESDD using salt solutions 



NaCl 


e 


tfe 


G20 


S a 


ESDD 


15 


28 


0.114 


0.0966 


0.000439 


0.0396 


20 


28 


0.165 


0.1398 


0.000643 


0.0579 


25 


28.5 


0.205 


0.1718 


0.000795 


0.072 


30 


28 


0.315 


0.2669 


0.001252 


0.1128 



The correction conductivity and ESDD obtained from the 
present measurements by varying the amount of salt 
concentration is presented in Fig. 3. 

0.4 
>. 0.35 




15 



20 25 30 

Salt concentration in g/Lr 



Fig. 3. Variation of ESDD and Conductivity with salt concentration 

O.Ofi 

£ O.O* 

0.0 i 

0.0: 

D 




0.0? 



0.1* 



0.17 0.2* 

CoudiicliYitv 



Fig. 4. Variation of ESDD with conductivity 



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In Fig. 3, for the salt concentration from 15g to 30g, the 
variation of conductivity and ESDD is almost linear. The 
values of ESDD are also plotted versus the conductivity which 
is shown in Fig. 4. It is observed that the relationship between 
the conductivity and ESDD is linear [10]. 

B. Leakage Current 

Table IV Experimental test result 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
reach the critical value, the flashover is imminent 
[3] [11]. 



Applied 
voltage(V) 


NaCl 
Qty(g) 


Leakage Current(mA) 


Status of 
Flashover 


Initial 
Value 


Maximum 
Value 


230 


15 


33 


42 


Withstand 


230 


20 


58 


96 


Withstand 


230 


25 


90 


130 


Withstand 


230 


30 


106 


220 


Flashover 



Table V Test results for various pollution levels 



NaCl 
Qty(g) 


Leakage Current(mA) 


ESDD 

(mg/cm 2 ) 


Pollution 
Level 


Initial 
Value 


Maximum 
Value 


15 


33 


42 


0.0396 


Light 


20 


58 


96 


0.0579 


Light 


25 


90 


130 


0.0725 


Medium 


30 


106 


220 


0.1128 


Heavy 



vi. Using the proposed model, the leakage current 
were measured and found similar [3] [4] from 
experiment. These revealed the equivalent model 
with 230V supply could be used for flashover 
prediction and analysis in the place of the 
standard high voltage insulator. 

VI. Conclusion 

To simplify the mathematical analysis, tests were done on a 
flat trough model of simple geometry. The maximum leakage 
current and the conductivity of the contaminant solution on the 
surface of the model and the corresponding ESDD of various 
degree of pollution were also determined. The laboratory 
model test results either in a flashover or a withstand. The 
result also showed that the leakage current strongly correlated 
with insulator polluted level and to assess the condition of the 
insulation system which indicates the occurrence of a 
flashover situation and the need for cleaning of the insulators 
when it exceeds the medium level of ESDD. 

Even though the model presented above still needs 
modifications, the extent of study of leakage current 
magnitude for the analytical case is satisfactory. At low 
voltage, the model tests can be standardized easily; its use in 
studying contamination flashover should be encouraged. In 
this way, fairly accurate results could be obtained eliminating 
the need of site testing. 



The following points were observed from the results shown 
above in Table IV and V. 

i. For light contamination of 15g NaCl, the 
maximum leakage current measured is 42 mA. 
The calculated ESDD from the contamination 
deposit is 0.0396 which indicates the light degree 
of pollution. 

ii. For 20g NaCl, maximum leakage current is 
96mA which results in withstand and the 
corresponding ESDD of 0.0579 also indicates the 
low level of pollution. 

iii. For 25g the leakage current reaches a peak of 
130mA the field exceeds the withstand capability, 
initiates an arc discharge and extends several 
arcing which is actually preceded before the 
flashover. ESDD value is 0.0725 shows the 
medium level of pollution. 

iv. For 30g the leakage current measured is 106mA 
and reached a peak of 220mA. It results in flash 
over after 10 minutes of wetting. ESDD shows 
the heavy pollution level for 0.1 128. 

v. The leakage current shows that the pollution 
severity can be correlated with the surface 
conductivity and ESDD. When these quantities 



References 



[1] Ebenezer Jeyakumar, "Development of verisimilar juxtaposition model 
and study of physical phenomena on polluted insulators, " PhD 
Dissertation, Department of Electrical Engineering, Anna University 
Madras, India, June 1991. 

[2] B. F. Hampton, "Flashover mechanism of polluted insulation," 
Proc.IEE, vol.111, pp.985-990, 1964. 

[3] M.P.Verma, "Die quantitative erfassung von fremdschichichteintlusse," 
ETZ-A97, pp. 281-285, 1976. 

[4] George Karady, Felix Amarh, Raji Sundararajan, "Dynamic modeling of 
ac insulator flashover characteristics, " 1 1th International Symposium of 
High Voltage Engineering, Vol. 4, pp. 107-110, London, England, 
August 1999. 

[5] M.A.M. Piah, Ahmad Dams, "Modeling leakage current and electric 
field behavior of wet contaminated insulators," Power Engineering 
Letters, IEEE Transactions on Power Delivery 19 (1) 432-433, January 
2004. 

[6] I.R. Vazquez, G.M. Tena, R.H. Corona, "Nonstandard method for 
accelerated aging tests of non ceramic insulators, " TEE Proceedings - 
Generation Transmission and Distribution 149 (4), 439-445, July 2002. 

[7] F. V. Topalis, I. F. Gonos and I. A. Stathopulos, "Dielectric behavior of 
polluted porcelain insulators, " TEE Proc.-Gener. Transm. Distrib., Vol 
148, No. 4, pp. 269-274, July 2001. 

[8] IEC 60507, "Artificial pollution tests on high voltage insulators to be 
used in ac system, " Switzerland, 1991. 

[9] IEC60815, "Guide for the selection of insulators in respect of polluted 
conditions, " 1986. 

[10] M. A. Salam, N Mohammad, Zia Nadir, Ali Al Maqrashi, A Al Kaf, 
"Measurement of conductivity and equivalent salt deposit density of 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
contaminated glass plate, " IEEE conference TENCON, Vol.3 pp 268- 
270, 2004. 

[11] Guan Zhicheng, Cui Guoshun, "A study on the leakage current along 
the surface of polluted insulator," Properties and applications of 
dielectric materials, Proceedings of the 4 th international conference on 
volume 2, 3-8 July, pp.495-498,1994 



1 58 http://sites.google.com/site/ijcsis/ 

ISSN 1947-5500 



(IJCSIS) InternationalJournal of Computer Science and Information Security, 

Vol. 8, No. 3, June 2010 

SSL/TLS Web Server Load Optimization using 
Adaptive SSL with Session Handling Mechanism 

R.K.Pateriya # , J.L.Rana # , S.C. Shrivastava # 
department of Computer Science & Engineering and Information Technology 

Maulana Azad National Institute of Technology, Bhopal, India 
Emails: pateriyark@gmail.com Jl_rana@yahoo.com , scs_manit@yahoo.com 



Abstract — Secure Sockets Layer (SSL) is the world standard for 
web security. SSL provide authentication ,data integrity and ensure 
message confidentiality using cryptography. This paper proposes 
an approach for load management by applying Adaptive SSL 
(ASSL) policy with session handling mechanism for enhancement 
of the security and performance of the server . ASSL policy 
negotiate session security at runtime by adapting more secured and 
comparatively costly cryptographic algorithm at runtime if load is 
under safe limit otherwise change to less secure algorithm .Session 
handling mechanism limit the active session running on the server 
.This self-adaptive security policy offers great potential in providing 
timely fine grained security control on server and therefore 
enhance performance and security of e-commerce sites. 



Keywords- Admission control, 
Security, SSL Session. 



E-commerce, Overload control, 



I. Introduction 

Security between network nodes over the Internet is 
traditionally provided using HTTPS i.e. SSL (Secure Socket 
Layer).It perform mutual authentication of both the sender and 
receiver of messages and ensure message confidentiality. This 
process involves certificates that are configured on both 
sides of the connection. Although providing these security 
increases remarkably the computation time needed to serve a 
connection, due to the use of cryptographic techniques. The 
disadvantage of the basic SSL approach is that security needs 
to be preconfigured on only predefined static information, 
namely the data and its location, that can be utilized when 
making renegotiation decisions. Adaptive SSL extends SSL 
beyond these limitations. 

Adaptive security model was proposed in [1] which 
provide appropriate security mechanisms for SSL sessions at 
any particular moment in time as the environment changes. 
The adaptation controller for SSL called Adaptive SSL. 
concerned with adapting the choice of cryptographic 
algorithms applied to client-server interactions. [2] 
experimentally investigated the effects of security adaptation 
in various client-server scenarios . 

In [3,4,5,6,7,8] various session based admission control 
(SBAC) mechanism for load management are discussed. 
Admission control is based on reducing the amount of work 
the server accepts when it is faced with overload. On most of 



the prior work, overload control is performed on per request 
basis, which may not be adequate for session-based 
applications, such as e-commerce applications. Session 

integrity, load management and security are the critical issue 
for the success of e-commerce site. 

This paper proposed a frame work for enhancement of 
server and e-commerce application performance through load 
management using Adaptive SSL (ASSL) policy with session 
handling mechanism .This Adaptive SSL facilitates runtime 
adaptation of the SSL protocol by effectively intercepting 
requests between the client and SSL module and based on a 
set of conditions, may decide to renegotiate the session 
security i.e. selecting appropriate cryptographic algorithm at 
runtime. This self adaptive security will enhance performance 
of server as it reduces some of the overhead related to 
cryptographic algorithm used for secure connection. 

The rest of the paper is organized as follows: Section 2 
introduces the SSL protocol. Section 3 presents Overview of 
session based admission control techniques. Sections 4 
introduces ASSL and Section 5 define proposed approach and 
Section 6 provide experimental environment detail and 
section 7 presents the conclusions of this paper. 



//. 



SSL PROTOCOL 



The SSL .protocol provides communications privacy over 
the Internet. It uses combination of public-key and private-key 
cryptography algorithm and digital certificates to achieve 
confidentiality, integrity and authentication. The SSL protocol 
increases the computation time necessary to serve a 
connection remarkably due to the use of cryptography to 
achieve their objectives. The study concludes that SSL 
connections is 7 times lower than when using normal 
connections as discuss in[7,8]. 

The four protocol layers of the SSL are Record Layer, 
ChangeCipherSpec Protocol, Alert Protocol, and Handshake 
Protocol ,they encapsulate all communication between the 
client machine and the server. 



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HTTP 



SMTP 



IMAP 



F1P 



LI-;. 






HaeShfr 






MHtPMml 



AHlltiliM 



Record Layer Protocol 



TCP 






(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, June 2010 



handshake is negotiated when a client establishes a new 
HTTP connection with the server but using an existing SSL 
connection. As the SSL session ID is reused, part of the SSL 
handshake negotiation can be avoided, reducing considerably 
the computation time for performing a resumed SSL 
handshake. Notice that their is big difference between 
negotiate a full handshake respect to negotiate a resumed SSL 
handshake (175 vs. 2 ms) as discussed in[7,8]. 

SSL Connection 



Fig 1 SSL Protocol 
The SSL handshake allows the server to authenticate itself to 
the client using public-key techniques like RSA and then 
allows client and the server to cooperate in the creation of 
symmetric keys used for rapid encryption, decryption. 
Optionally the handshake also allows the client to authenticate 
itself to the server. 

1: ClientHello 



2: ServerHello 



3: Certificate (optional) 
4: Certificate Request (optional) 

^ 1 

^ 5: Server Key Exchange (optional) 
6: ServerHelloDone 



Client 



7: Certificate (optional) 



"* Server 



8: Client Key Exchange 

p 

9: Certificate Verify (optional) 
10: Change Cipher Spec 

11: Finished 

1 

12: Change Cipher Spec 

13: Finished 

i 

14: Encrypted Data 



Fig 2 Handshake Protocol 

Two different SSL handshake types can be distinguished: 
The full SSL handshake and the resumed SSL handshake. The 
full SSL handshake is negotiated when a client establishes a 
new SSL connection with the server and requires the complete 
negotiation of the SSL handshake, including parts that spend a 
lot of computation time to be accomplished. The SSL resumed 



This is a logical client-server link, associated with the 
provision of a suitable type of service. In SSL terms, it must 
be a peer-to-peer connection with two network nodes. Every 
connection is associated with one session . 

SSL Session 

This is an association between a client and a server that 
defines a set of parameters such as algorithms used, session 
number etc. An SSL session is created by the Handshake 
Protocol that allows parameters to be shared among the 
connections made between the server and the client and 
sessions are used to avoid negotiation of new parameters for 
each connection. This means that a single session is shared 
among multiple SSL connections between the client and the 
server. 



///. 



OVERVIEW OF SBAC TECHNIQUE 



Following section give comparative study of various 
session based admission control mechanism for load 
management as discussed in [3,4,5,6,7,8] 

CPU utilization based implementation presented in [3,4] is 
the simplest implementation of session based admission 
control but can break under certain rates and not work 
properly, reason is that the decision , whether to admit or reject 
new sessions, is made at the boundaries of ac-intervals and 
this decision cannot be changed until the next ac-interval. 
However, in presence of a very high load, the number of 
accepted new sessions may be much greater than a server 
capacity, and it inevitably leads to aborted sessions and poor 
session completion characteristics 

Hybrid admission control strategy covered in [5] which 
tunes itself to be more responsive or more stable on a basis of 
observed quality of service. It successfully combines most 
attractive features of both ac-responsive and ac-stable policies. 
It improves performance results for workloads with medium to 
long average session length. 

Predictive admission control strategy also covered in [5] 
which estimates the number of new sessions a server can 
accept and still guarantee processing of all the future session 
requests. This adaptive strategy evaluates the observed 



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workload and makes its prediction for the load in the nearest 
future. It consistently shows the best performance results for 
different workloads and different traffic patterns. For 
workloads with short average session length, predictive 
strategy is the only strategy which provides both: highest 
server throughput in completed sessions and no (or, practically 
no) aborted sessions. 

Session-based adaptive overload control mechanism based 
on SSL connections differentiation and admission control 
presented in [7,8] prioritizes resumed connections maximize 
the number of sessions completed and also limits dynamically 
the number of new SSL connections accepted depending on 
the available resources and the number of resumed SSL 
connections accepted, in order to avoid server overload. 



IV. 



ADAPTIVE SSL 



SSL protocol used to secure the communication channel 
between an application or web server and a client. The basic 
SSL secure transport layer connection, is established through a 
handshake mechanism where algorithms are selected based on 
those available to both the client and the server, for providing 
the three security properties confidentiality, authentication 
and data integrity. This process is commonly known as SSL 
negotiation and the resulting connection is called a session. 
Once established, the session can conduct a renegotiation, 



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n 


1*7* 


Am 


K 


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(IJCSIS) InternationalJournal of Computer Science and Information Security, 

Vol. 8, No. 3, June 2010 
The disadvantage of the basic SSL approach is that security 
needs to be preconfigured only on predefined static 
information, namely the data and its location, that can be 
utilized when making renegotiation decisions. 

Adaptive SSL extends SSL beyond these limitations. 
Previous work on Adaptive SSL (ASSL) covered in [1,2] 
facilitates runtime adaptation of the SSL protocol. ASSL aims 
to provide appropriate security mechanisms for SSL sessions 
at any particular moment in time as the environment changes. 
Attributing environmental factors could include the threat 
level, server load or transaction type. Client attributes such as 
processing power, bandwidth or type of client can also be 
considered. These factors are monitored by specialized third 
party applications that inform ASSL of affected clients and 
appropriate security measures. In [1,2] introduces a generic 
design for controlling the renegotiation within SSL and the 
resulting system called as 'Adaptive SSL'. 
Design of Adaptive SSL 

ASSL introduces a flexible approach to session 
management where renegotiation logic is decoupled from the 
main SSL implementation and web server configuration. 
Separating these concerns enables us to build a more powerful 
adaptive security model since the renegotiation rules can be 
determined and deployed independently and parallel to the 
web server and its components. 

The ASSL module described in [1,2] effectively intercepts 
requests between the client and SSL module and based on a 
set of conditions, may decide to renegotiate the session 
security. The set of conditions are specified and altered at 
runtime by 'third parties' that is, other programs such as 
firewalls, system performance monitors, network monitors, 
server administrators, etc.. ASSL is used to create a self- 
adaptive security solution based on flexible use of 
renegotiation in SSL. 



Fig 3 Basic SSL Model 

(enc=encrypted, req=request, resp=response, conf=configuration) 

Fig. 3 taken from [1,2] depicts a standard web server 
request-response processing cycle during an SSL secured 
session. The numbers in the figures indicate the event order 
and the labels the interaction type. Events with the same 
number indicate a decision point and only one of the events 
take place. It shows the client sending an encrypted request to 
the server in step 1. The request is initially passed to SSL 
which then queries the web server's configuration file (where 
the renegotiation decision rules are typically stored) and 
decides either to process the request or renegotiate the session. 




Fig 4 Adaptive SSL 

Figure 4 taken from [1,2] explain how ASSL handles SSL 
requests. It effectively takes over the SSL negotiation and 
renegotiation logic by intercepting requests and evaluating 
them against the third party input. Decoupling the negotiation 
logic in this way from the main server logic, which is pre- 
configured in the "Web Server Configuration" allows to build 



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a more powerful and flexible adaptive security model. 
Adaptive Policy 

Adapting security based on current system performance is 
explored in [1,2] . In [1] it was shown that Adapting the 
security, i.e. changing the security algorithm at runtime has a 
server performance impact .It has investigated the different 
aspects of client load patterns. 

In [2] a policy is defined to trade-off security and 
performance and shows how it performs in the scenario and 
finally experiment shows that the performance implications 
of adapting the security is highly dependant on the SSL 
session length and the requested file size. In [2] it was shown 
that performance cost incurred by the server through 
encryption and decryption is significantly influenced by the 
particular software implementation ,more costly algorithms 
should result in lower server throughput i.e 3DES is the most 
costly algorithm and is about 16 times more expensive than 
RC4 which is the least secure . 

Table 1 Average number of bytes processed per second 



(IJCSIS) InternationalJournal of Computer Science and Information Security, 

Vol. 8, No. 3, June 2010 
server could cope with the same load at a higher security level. 
When security is adapted only new clients start with the higher 
level of security and so it takes some time before the server is 
serving all clients at this new level . 



Cryptographic 
Algorithm 


Throughput 


RC4 


112000 


AES 128 


36000 


AES 192 


30600 


AES 256 


26500 


DES 


19300 


3DES 


6900 



Based on this fact following adaptive policy is discussed in[2] 

Pseudo code 

#comment 

load = getCpuLoad 

PolicyTable(row, column) 

curSec #current Security level 

newSec #new Security level 

#Move to X 

Move to PolicyTable(curSec,curSec) 

#Check if security should be increased 

#Check all values to the left 

IF load < PolicyTable(curSec, curSec - n) 

RECORD newSec 

#Check if security should be decreased 

#Check all values to the right 

IF load >= PolicyTable(CurSec, CurSec - n) 

RECORD newSec 

IF newSec THEN adapt 

#end 

This adaptation policy is very robust to client behavior. 
Firstly, security is reduced as soon as the server reaches its 
maximum load.. Secondly, security is only increased if the 



Application Area 

Applications for adaptive security have been proposed in a 
number of diverse areas. Examples include mobile ad-hoc 
networks where current network conditions play a role in 
choosing relevant security protocols at runtime . Media 
streaming research proposes the use of additional contextual 
information, in the form of an intelligent routing 
infrastructure, to allow for flexible security levels . There have 
also been proposals to provide adaptive system security 
through system event monitoring . 

This Adaptive SSL research is preparatory work to create a 
self-adaptive security solution based on flexible use of 
renegotiation in SSL. Adaptive SSL policy is applied in 
different realistic scenario and establish strategies and 
optimizes the trade-off between security and performance 
(overhead as well as server performance) when renegotiating 
security level. The proposed work is discussed in following 
section. 



V. 



PROPOSED APPROACH 



The proposed work utilizes session handling mechanism 
with adaptive SSL policy for load management. This policy 
provides more flexible approach to session management where 
renegotiation logic is decoupled from the main SSL 
implementation and web server configuration. Runtime 
adaptation of cryptographic algorithm on online e-commerce 
site based on current number of active session help in 
increasing the number of completed session and enhancing 
server and application performance. 
Phases of proposed framework 

Server Authentication 

Session Monitoring 

Application Of Adaptive SSL Policy 

Testing of Server and Application Performance 

Phase 1: Server Authentication 

Security between network nodes over the Internet is 
traditionally provided using Secure socket Layer .For 
communicating through SSL, server authentication is 
mandatory, the client tries to confirm the identity of the server 
based on the server's certificate. To accomplish this, on 
receiving a request from the client, the server sends its 
certificate to the client. This certificate contains information 
such as: server's public key, certificate's serial number, 
certificate's validity period, server's distinguished name, 



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issuer's distinguished name, and issuer's digital signature (a 
message signed using the issuer's private key). The client, on 
receiving this certificate, authenticates the server.. 

For performing the above authentication, the server must 
have a Public Certificate and Key store containing the key. A 
certificate can be self-signed when authentication over the 
internet is not really a concern, that is only data privacy and 
integrity are important .So self signed certificate is generate or 
certificate request is sent to CA for trusted certificate .So our 
work starts with generation of private and public key and self 
signed certificate and export it into server configuration file 
and hence configuring secure socket layer on server. 

Phase 2: Session Monitoring 

Session integrity is the critical metrics for e-commerce. So 
load management through session handling is performed 
which will enhance application performance. Firstly create a 
web application and deploy it on server. Then monitor the 
number of active session on that application and check against 
maximum limit as new session is created, if the maximum 
limit is reached , then do not allow the new session.. 

Phase 3 : Application Of Adaptive SSL Policy 

The proposal is to design a system for client server 
interaction which will monitor active sessions on the server 
and when the number of session reaches to alarming level then 
change highly secure cryptographic algorithm used for https 
communication to the algorithm which is less secure and 
consume less time in encryption and decryption process. This 
approach help in reduction of some load on the server due to 
less time consumption during cryptographic process. We are 
using security trade off between 3DES and RC4cryptographic 
algorithm. 3DES is more secured but gives least throughput 
and RC4 is the weak steam cipher but with good throughput 
capability. 

Triple- Data Encryption Standard (3DES) 

Triple-DES Using standard DES encryption, encrypts data 
three times and uses a different key for at least one of the three 
passes giving it a cumulative key size of 112-168 bits. The 
Triple-DES is considered much stronger than DES, however, 
it is rather slow compared to some new block ciphers hence 
give poor throughput as compared to other cryptographic 
algorithm. 

RC4 Stream Cipher 

RC4 is a cipher invented by Ron Rivest, co-inventor of the 
RSA Scheme.RC4 has two advantages over other popular 
encryption algorithms. First, RC4 is extremely fast. Second, 
RC4 can use a broad range of key lengths. For most ciphers, 
longer key length is better. However, RC4 was widely used 
primarily because its shortest optional key length is 40 bits. 
Unfortunately, RC4 is a dangerous cipher to use. If it is not 
implemented perfectly, its protection is minimal. 



(1JCS1S) InternationalJournal of Computer Science and Information Security, 

Vol. 8, No. 3, June 2010 
Phase 4: Testing of Server and Application Performance. 

This phase will analyze our approach and show its 
importance in client /server paradigm. The main aim is to 
compare the throughput and response time of the application 
under different load pattern and in two different environment , 
which uses different cryptographic algorithm for security. This 
phase also provides visualization of the various performance 
metrics which can be utilized for further result analysis. In 
this phase there are two measures for evaluating performance 
(i) Throughput and (ii) Response Time 



VI. 



Conclusion 



This proposal of runtime adaptation of cryptographic 
algorithm using session handling mechanism will enhance 
throughput and response time of server . Session handling 
combines with Adaptive SSL is good alternative for load 
management. This work will provide self adaptive security 
control on server and optimizes trade off between security and 
performance. 

ACKNOWLEDGMENT 

The Success of this research work would have been 
uncertain without the help and guidance of a dedicated group of 
people in our institute MANIT Bhopal. We would like to 
express our true and sincere acknowledgements as the 
appreciation for their contributions, encouragement and 
support. The researchers also wish to express gratitude and 
warmest appreciation to people, who, in any way have 
contributed and inspired the researchers. 

REFERENCES 

[1] C. J. Lamprecht , Aad P. A. van Moorsel, "Adaptive SSL: 

Design, Implementation and Overhead Analysis", prdc, 

pp.289-294, First International Conference on Self-Adaptive 

and Self-Organizing Systems (SASO '07), 2007. 

[2] C.J. Lamprecht, Aad P. A. van Moorsel, "Runtime Security 

Adaptation Using Adaptive SSL," prdc, pp.305-312, 14th 

IEEE Pacific Rim International Symposium on Dependable 

Computing, 2008 

[3] L. Cherkasova, P. Phaal "Session Based Admission 

Control: a Mechanism for Improving the Perfor-mance of an 

Overloaded Web Server." HP Laboratories Report No. HPL- 

98-119, June, 1998. 

[4] L. Cherkasova, P. Phaal "Session Based Admission 

Control: a Mechanism for Improving Performance of 

Commercial Web Sites." prcd, Seventh International 

Workshop on Quality of Service, IEEE/IFIP event, London, 

1999. 

[5] L. Cherkasova, P. Phaal "Session Based Admission 

Control: a Mechanism for Peak Load Management of 

Commercial Web Sites." IEEE J. Transactions on Computers, 

Vol. 51, No. 6, June 2002. 

[6] M. Arlitt, "Characterizing Web User Sessions", ACM 

SIGMETRICS Performance Evaluation Review, Vol. 28, No. 

2, pp. 50-56, September 2000. 



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Vol. 8, No. 3, June 2010 
[7] Jordi Guitart , David Carrera , Vincenc Beltran , Jordi 
Torres , Eduard Ayguade, "Session-Based Adaptive Overload 
Control for Secure Dynamic Web Applications" Proceedings 
of the 2005 International Conference on Parallel Processing, 
pp.341-349, 2005. 

[8] Jordi Guitart , David Carrera , Vicenc Beltran , Jordi 
Torres , Eduard Ayguade, "Designing an overload control 
strategy for secure e-commerce applications," Computer 
Networks: The International Journal of Computer and 
Telecommunications Networking, v.51 n.15, p.4492-4510, 
October, 2007 

[9] P. Lin, "So You Want High Performance" (Tomcat 
Performance), September 2003, online available URL: 
http ://j akarta. apache . org/tomcat/articles/performance .pdf . 
[10] Sun Microsystems, "Java Secure Socket Extension 
(JSSE)." online available URL: 

http://j ava.sun.com/products/j sse/. 

[11] Jakarta Project. Apache Software Foundation, Jakarta 
Tomcat Servlet Container. URL: 

http://jakarta.apache.org/tomcat. 

[12] A.O. Freier, P. Karlton, C. Kocher, "The SSL Protocol 
Version3.0" November 1996. available online URL: 
http://wp.netscape.com/eng/ssl3/ssl-toc.htm 




R K Pateriya M.Tech & B.E. in Computer 
Science & Engg. and working as Associate 
Professor in Information Technology 
Department of MANIT Bhopal . Total 17 Years 
Teaching Experience ( PG & UG ). Guided 
twentv M.Tech Thesis . 



Dr. J. L . Rana Professor & Head of Computer 
Science & Engg deptt. in MANIT Bhopal .He 
has received his PhD from NT Mumbai & M.S. 
from USA (Huwaii) .He has Guided Six PhD. 



Dr. S. C. Shrivastava Professor & Head of 
Electronics Engg. department of MANIT 
Bhopal. He has Guided three PhD , 36 
M.Tech and presented nine papers in 
international & twenty papers in national 
conference in India. 



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An Enhancement on Mobile TCP Socket 



S.Saravanan, 
Research Scholar 
Sathyabama University 
Chennai, India 



Dr.T.Ravi 

Professor & Head, Dept. of CSE 
KCG College of Technology, 
Chennai, India 



Abstract - A TCP session uses IP addresses (+ IP port) of 
both end points as identifiers. Therefore when a mobile 
handover to a new AP that belong to a different 
subnet/domain, the IP address will changes and ongoing 
TCP connections are reset. Several approaches have been 
proposed to solve this problem, and one of which was to 
modified the TCP/IP stack to update the changes of the IP 
address for the ongoing connections [5] [6]. However, these 
proposals causes unnecessary processing when TCP is used 
in applications which have already employed some kinds of 
security measures, such as SIP. This paper proposes the 
Mobi Socket, which specifically supports TCP mobility for 
intrinsic secure applications without unnecessary overhead. 

1. INTRODUCTION 

TCP/IP was developed when all network nodes 
were stationary, and connection to a network is through 
cable, therefore it is unthinkable that a node will move to 
another subnet while ccessing to the Internet, and the IP 
address of an end host is assumed to stay unchanged while a 
computer is running. As a result, IP address (together with IP 
port) is used as identifiers for TCP session, and the TCP layer 
at the end host maintains TCP control blocks (TCBs), which 
hold the IP addresses and IP ports of both ends for each TCP 
connection to fmd the right socket for each datagram it 
receives from the IP layer. 

But with the introduction of wireless access 
technologies such as Wi-Fi, it is possible for a mobile node to 
handover to a new AP that belong to a different 
subnet/domain while actively connecting to the Internet. This 
causes an IP address change, and for current implementation 
of TCP/IP stack, all ongoing TCP connections are reset. This 
will cause problem for long-live TCP sessions. 

There are two general approaches to solve the 
problem of changing host IP address for TCP session. The 
first one uses the split-connection approach, which introduces 
a fix middle agent between the mobile host (MH) and 
correspondent host (CH) [4]. The connection between CH 
and MH is broken into two parts, the fixed part between the 
CH and the agent remains unchanged regardless of the 
position of the MH, and the TCP connection between the 
agent and the MH will be re-established whenever the MH 
handovers to a new address. In this sense only the TCP at the 
MH is affected, while at the CH the TCP session is not 
disturbed. The problems of these approaches are non- 
transparent end-to-end operation of TCP session, as well as 
the requirement of new infrastructure entities (the middle 
agent) and triangle overhead. 



The other approach modify the TCP stack so that when 
the mobile host changes the connection to the internet, the 
TCP stacks at both ends preserver the TCP connection and 
update the TCBs with the new IP address at both ends 
accordingly. 

In [5], when the MH changes its location, the proposal 
in [5] introduced new states to the TCP specification. When 
the address of MH changes, MH and CN will exchange 
information and update the new IP address accordingly. 
Both sides will prepare in advance a share- secret, and use 
this sharesecret to authenticate each other during the update 
process. 

The proposal in [6] employs a similar concept, but 
instead of changing the TCP stack, it uses kernel extensions 
and a userlevel redirect daemon process (this was the design 
of the prototype in BSD). The daemon process will monitor 
the wireless network interface for changes of IP address, 
and if one is detected, the daemon at the MH will inform the 
counterpart at the CH to update the new IP address together. 
To secure the update process from malicious acts, MH and 
CH also need a share- secret in advance. 

The problem with [5] is that both sides has to perform 
additional works to exchange a share- secret in advance, 
regardless of whether the MH will actually performs the 
handover to a new Access Point (AP) or not, or whether the 
TCP session lives long enough to experience a handover. 
The proposal in [6] relieves this matter by initiating the 
preparation process only if the TCP connection exists longer 
than a threshold. However, if the MH does not perform a 
handover, then all of the preparations for the long-live 
connections are wasted. 

One more problems with [5] and [6] is that processing 
the share- secret for authentication will requires a lot of 
processing, which in turn consumes battery power at the 
MH. If many TCP connections are used (such as if the user 
constantly browsing the Internet) then battery life will be 
shortened considerably. Moreover, both [5] and [6] are not 
applicable in the case where both ends perform handover 
simultaneously. 

In the next parts of this paper, we propose a new type of 
socket called the TCP MobiSocket, that remains connected 
even if the concerned IP address changes. It works like 
normal TCP socket, but does not get reset when the IP 
address at either end changes, and with an additional 
updateTCB() member function to update the TCBs with the 
new address. All of the security issues that are required to 
secure the update of the new address will be handled by the 
calling applications. This new socket is dedicated to support 



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mobile TCP session for intrinsically secure application, 
without all the above mentions problems of [5] and [6]. 

2. DESIGN OF THE MOBISOCKET 

Logically, there are two phases when mobile device 
handover. First, the Network interface/card disconnects from 
the old AP. Then it connects to the new AP. In traditional 
TCP stack, the network stack at the MH will close all TCP 
connections in cleaning-up activities, as well as reset the 
TCBs during these phases. 

On the other hand, all of the ongoing MobiSocket will 
remain in ESTABLISHED state, when the IP address 
changes, waiting to be updated by the application. 

We design a new socket that allows the application to 
update the change of PoA at both end hosts. The socket is 
designed based on the following assumptions/requirements: 

• There are cases when the TCP connection needs explicit 
handling before communicating using the new IP 
address (Re-establishment/update of Security 
Association for VPN, sending the PATH message of 
RSVPforQoS, etc... ) 

• The application takes care of security activities regarding 
the update of the new address. The reasons for this are 
(1) if the connection needs to be secure, the applications 
have already shared some kind of security, and (2) if the 
connection is not important to the extend that it requires 
a shared security association between both end host, then 
it might not important enough to be hacked by others. 

• Compatibility with applications using legacy TCP/IP 
stack is desired to promote deployment. It means that in 
the case the other end does not support the features of 
mobile socket, connection will work according to that of 
legacy TCP specification. 

• Being able to provide handover of TCP session between 
different network interfaces of the same mobile device. 
The requirement is that not only IP address but change 
of TCP port also must be supported, because the same 
port of the other network interface might be in use by a 
different application at the time of the request for 
handover. 

The application which uses the MobiSocket will call the 
MobiSocket's updateTCB() member function to update the 
TCB with the new destination address. To satisfy the above 
requirements, the mobile socket will provide the following 
APIs to the applications: 

• acvMobi(socket_id) 

socket id: the handler of the socket 

■ The application will call this function to explicitly 
activate the mobile feature of the socket 

■ If this function is not called, then the MobiSocket 
will work like normal TCP socket 

■ When this function is called, the TCP connection 
will not be abolished if the concerned wireless 
interface changes to a new IP address 

• updateTCB(socket_id, direction, newIPaddress, 



newPort) 

socket id: handler of the socket 

direction: update the source or the destination address 

newIPaddress, newPort: the new IP address and new port 

to update to TCB/PCB (TCP Control Block/Protocol 

Control Block). If the port is then keep the existing port 

value 

■ The application will call this function to update the 
TCBIPCB (TCP Control Block/Protocol Control 
Block) with the new source/destination address and 
port 

■ The MobiSocket will start a new congestion 
control algorithm called the mobile congestion 
control 

• copyTCB(new_socket_id, old_socket_id) 
old socket id: handler of the old socket 

new- socket- id : handler of the new socket 

■ The application will call this function to update the 
TCBIPCB (TCP Control Block/Protocol Control 
Block) of the newly created socket with the 
information of the old socket. This is used when 
the application want to handover from old interface 
to new interface. 

■ This function will copy all information of the old 
socket (include current states, CWND, AWND, 
RTO etc., except the source IP address and source 
Port) to that of the new socket, and then delete the 
old socket without sending FIN to the other end 
(i.e., application at the CH). 

Apart from the above two new APIs/member 
functions, the MobiSocket also introduces two new 
message, the AddChange and AddConfirm. 

The AddChange contains (I) A shared token between 
Mobile Host (MH) and Correspondent Host (CH), (2) the 
old IP address and the new IP address encrypted by the 
private key of the MH, (3) the new port address and (4) The 
old IP address of the MH in plain-text. 

The AddCorifirm contains (1) the shared token between 
MH and CH, (2) the new IP address encrypted by the 
private key of the CH. 

If the two messages above are implemented as TCP 
header options, then these header options must be sent to the 
applications, but currently there is no mechanism to perform 
such action. Therefore, it might be better to send this as 
OOB (out-of-band) data using the TCP Urgent Pointer . 

3. WORKING PROCEDURE OF THE MOBISOCKET 

Let's consider the use of MobiSocket for a SIP 
application. Suppose that a TCP connection is established 
between MH and CH (the thick, solid line), which have 
established a SIP session through the SIP server. The 
MobiSocket will work as follows (see figure 1): 

• First the application creates the TCP socket for the SIP 
session, and calls the acvMobi () to activate the mobile 
feature for the socket 



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In step <1>, the MH moves from Subnet 1 to Subnet 2, 

and in the process its address change from IPaddress 1 to 

IPaddress2 

In step <2>, the SIP application at the will call the 

updateTCB () function to replace IPaddressl with 

IPaddress2 at the TCB table. Then it issues a SIP INFO 

message to ask CH update the new IP address of the 

MH. 

Upon receiving this INFO message in step <3>, the SIP 

application at the CH will authenticate the message 

using SIP security associations, and all the updateTCB () 

function to replace IPaddressl with IPaddress2 at the 

TCB table. 

Then in step <4> the SIP application at the CH will send 

back the INFO message back the the MH to confirm the 

change of address. Note that both INFO messages may 

contain other parameters of the concerned TCP session, 

such as new window size, MSS etc ... 

CH and MH will start sending data using the TCP 

connection when they receives the INFO message from 

the other end, and they will start receiving data after they 

send the INFO message to the other end. 



TCB table 

IP Src: MN's address 1 

IP Dest: CN address 



TCB table 

IP Src: MN' address 2 

IP Dest: CN address 



MH 
@ipaddress 




TCB table(before calling 

updateTCB) 

IP Src: MN's address 1 

IP Dest: MN's IP addressl 



© <* 



TCB table (after calling 

updateTCB) 

IP Src: MN's address 1 

IP Dest: MN's IP address2 



Figure 1 : Working procedure of the mobisocket . 



4. DISCUSSION AND OPTIMISATION 
The merits of the MobiSocket are: 

• Inherit intrinsic security feature of SIP 

■ Less processing overhead for security issues 
(conserve power) 

• Depending on the security requirements, the application 
can decide whether to allow the handover of TCP 
connection 

■ More suitable for application with strict 
security requirements 

• Still work when both ends handover simultaneously 

■ Reach-ability through SIP Registration 
functionality 

We can further optimize the operation of the MobiSocket 
as follows: 

When the MN receives the INFO message from CN, both 
ends might already time out (due to handover, NOT due to 
congestion), so even if the TCB is updated, no data 
exchange is possible until the time out is over (can be very 
long). We can provide a new function to reset the timer after 
the updateTCB O function, which is the reset'I'imensocket 
id). This function will reset the TCP socket to the state as if 
it has just received a datalACK packet from the other 
machine. 

Furthermore, if SIP proxy is used, then normally the MN 
has to finish re-Registration with the SIP proxy first before 
it can send SIP INFO message to the other end. This creates 
further delay for the TCP session. To solve this, we note 
that the MN and CN can share public key with each other 
during the initial INVITE process, therefore after the MN 
handover to a new IP address, it can use the public key of 
the CN to send the SIP INFO message to the CN right away. 
However, this solution cannot be used if both ends handover 
simultaneously (therefore they do not know the IP address 
of each other), in this case they must contact through SIP 
proxy server (after the re-Registration process) 

5. CONCLUSION 

In this paper we propose the MobiSocket to support 
TCP mobility for secure application such as SIP. This 
socket causes no overhead if handover does not take place 
like previous proposal, and moreover it still works when 
both side handover simultaneously. 

In this socket, there is no need for per-TCP connection 
authentication, because the authentication is left to 
application. Depending on the real situation, the application 
can also control whether to keep the TCP session or not, 
which is more appropriate for application which is applied 
with other application level constrains such as security and 
QoS policy . 

In the future, we would like to carry out the 
implementation of the MobiSocket to confirm the design of 
the system, as well as to measure the delay and throughput 
parameter when the reset'Iimen) function is (1) called and 
(2) not called, and compare the results with that of [5] and 
[6]. 



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We also would like to measure the delay in the case of 
SIP application, when we send the INFO message before and 
after re-Registration, as well as when two end hosts handoff 
together. 

We also plan to update the proposal in [1] with this 
new MobiSocket. 

REFERENCES 

[I] Vu Truong Thanh, Yoshiyori Urano, "Agent based LLMA 

handover scheme for SIP communication - The case for UDP 

traffic" , The II th International Conference on Advanced 

Communication Technology (rCACT), Feb. 2009 

[2] C. Perkins, "IP Mobility Support for IPv4", Request for 

Comments: 3344, IETF, August 2002 

[3] Huei-Wen Ferng et. ai, "A SIP-Based Mobility Management 

Architecture Supporting TCP with Handover Optimization", Proc. 

Of Vehicular Technology Conference, pp. 1224-1228, Apr. 2007 

[4] Milind Buddhikot et. ai, "MobileNAT: a new technique for 

mobility across heterogeneous address spaces", Proc. the 1st ACM 

international workshop on Wireless mobile applications and 

services on WLAN hotspot, pp. 75-84, Sept., 2003 

[5] FUNATO D., "TCP-R: TCP mobility support for continuous 

operation", Proc. IEEE International Conference on Network 

Protocols, pp.229 -236 ,Oct. 1997 

[6] Vassilis Prevelakis and Sotiris Ioannidis, "Preserving TCP 

Connections Across Host Address Changes", Lecture Notes in 

Computer Science, Springer Berlin / Heidelberg, pp. 299-310 Oct., 

2006 

[7] Rosenberg, et. ai., "Session Initiation Protocof, Request for 

Comments: 326 1 , IETF June 2002 

[8] D. Yon et. ai, "TCP-Based Media Transport in the Session 

Description Protocol (SDP)", Request for Comments: 4145, IETF, 

September 2005 





AUTHORS PROFILE 

S. Saravanan B.E., M.E., (Ph.D) working 
as an Assistant Professor at Jeppiaar 
Engineering College, Chennai and he has 
more than 9 years of teaching 
experience. His areas of specializations 
are Computer Networks, Network 
security and TCP/IP. 



Dr. T. Ravi,B.E,M.E,Ph.D is a Professor 
& Head of the Department of CSE at 
KCG college of Technology, Chennai. 
He has more than 18 years of teaching 
experience in various engineering 
institutions .He has published more than 
20 papers in International Conferences 
& Journals. 



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Technology in Education 

Modern Computer Graphics Technologies Used at 
Educational Programs and Some Graphical output screens 

*N. Suresh Kumar, 2 D.V. Rama Koti Reddy, 3 S. Amarnadh, 4 K. Srikanth, 5 Ch. Heyma Raju, 

6 R. Ajay Suresh Babu, 7 K. Naga Soujanya 

1,3,4,5 GIT GITAM University, Visakhapatnam 

2 College of Engineering, Andhra University, Visakhapatnam 

6 Raghu Engineering College, Visakhapatnam 

7 GIS, GITAM University, Visakhapatnam 

1 nskgitam2009@gmail.com 



Abstract In This paper a new technique is implemented to 
teach microprocessor and to clarify the doubts in the 
subject microprocessor. Although a lecturer has many aids 
to explain the topic in the class room, but a graphical 
environment is more power full environment in the present 
education scenario, which can improve the student level of 
understanding. The graphical interface develops the 
concepts of the student graphics concepts and also a student 
can easily grasp any level of task. The lecturer shows a 
visual object or animated show to the student to explain the 
particular topic in the class room. This work includes the 
framework of graphics programming; students can 
concentrate on the technical subject. Thus they acquire a 
method to construct computer graphics programs in many 
ways and gain knowledge in the concerned technical paper. 
The project have used for six years, and convinced of the 
positive effect. 

I Introduction 

E-learning substantially improves and expands the 
learning opportunities for students [3]. The modern 
computer information technologies, which are widely 
used both at educational programs for conducting of 
effective lecture, conducted scientific researches, and 
forming of practical and laboratory works with the 
students of technical and computer-based special[2]. 
Every teacher comes to understand that successful 
imparting of information and skills lies in the ability 
to incorporate a variety of technologies that, directly 
or indirectly, help communication between student 
and teacher [4]. Advances in learning objects and 
other technologies "that will optimize interoperability 
with other institutions and organizations in areas such 
as the creation of learning objects databases, 
information databases such as libraries, 
administrative systems and learner support strategies 
as well as the facilitation of interactions among 
learners and teachers", will continue to expand the 
scope of possibilities with which educational 
institutions will have to grapple [5]. 

II Experimental Idea 

The project was to develop software delivered 
diagnostic and remedial system for Diploma and 
Graduate students taking subjects in microprocessors 
and embedded systems. The system is comprised of a 
module to diagnose a student's background 



microprocessor knowledge and modules to which the 
student is directed to remedy any deficiencies that are 
found. The remedial modules focus on common areas 
of weakness in microprocessor architectures and 
interfacings and revise the basic peripherals that are 
needed to interface. For example, in memory 
interfacing the number of address lines and 
microprocessor type are incorporated with the graphic 
tools such as colors, sounds, messages and 
animations as shown in figure 1, 4, 5. When a student 
select any part on the selected architecture it will link 
to the particular file which give the details of the unit 
in the architecture as shown in figure 2, 3. The 
Interfacing devices like 8255 and it modes can be set 
in control register (CR) by an interactive message box 
as shown in the figure6. The software also 
demonstrates some of the bio-Medical applications 
and some interfacing designs such as traffic light 
controllers, stepper motor controllers, seven segment 
interfacing etc. This software allows the user to 
develop any interfacing circuit interactively as shown 
in figure 4. For maximum accessibility and ease of 
implementation, the software was designed to run in a 
Windows environment using graphics application in 
C language. This allows the software to be used off- 
campus, and makes the system equally accessible to 
students who are studying by distance education, 
open learning or who are located on an overseas 
campus [1]. 




Figure 1 8086 Architecture 



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Figure 2 Segment address 




Figure 3Access rights and Permissions 




Figure 4 Dialog box permits to set the memory 
settings by user 






|— > If W-> O 82B6 acts in BSR aoo> 


LL 


II II HI 

1 ^ LSB of port C nets if bit n'O'- o p or *l'->ixp 

1 > port B Acts if bit is'O'-Xo^p or *I'->i^p 

> MSB of port r. Art. << BCS ■■■pkA^aji _ •»'-*« -p 


YOU IMMT TO IMTI 


1 > port A Acts if bit is'O'-^p or «l*->ixp 

u_isc MR naoE<y^n^> activate <i/0 nocc><«> 



Figure 6 8255 CR msg box which allow the 
user to set the mode operations 




Figure 5 Memory Interfacing 



Figure 8 Bio medical Application, Heart Function 



Deficiencies in background knowledge and skills 
need to be diagnosed early before they become 
barriers to students' further learning in 
microprocessor subjects, it is not possible to offer 
individual tuition. The problem is exacerbated with 
off campus students taking Distance Education or 
Open Learning subjects. The software also help 
teaching staff relieved from from his duties for some 
time where he can attend other problems and enabling 
them to concentrate on presenting the primary subject 
material. The anticipated outcome will be improved 
student attitudes, and lower dropout and failure rates 
in a subject area which is difficult for many. Wade 
and Power [6] suggest the following "General 
Requirements for WWW Based Instructional 
Design:" 



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1) The presentation of material should support a 
range of sensory experiences incorporating 
interactivity and multimedia elements. 

2) Students should be provided with the opportunity 
to experiment with the knowledge they have 
learned. 

3) Testing and checkpoints are important from the 
point of view of repetition and student retention. 

4) Educational software should motivate the student. 

5) The learning environment should support the 
cognitive structures of the student; 

6) Facilities for synchronous communication and 
collaboration should be supported where possible. 

7) A well-designed interface will enable the student 
to interact with the material without the complex 
intermediaries and will aid in the understanding of 
the knowledge domain and structure. 

8) The development of a Tele-Educational course 
requires the support and cooperation of faculty 
and administration. 

9) WWW-based educational courses must be 
integrated into a well understood and explicitly 
specified curriculum this includes clear objectives, 
content description, methods of teaching, student 
learning, and student assessment and course 
evaluation. 

In the present paper the work includes two stages 
Stage 1: 

• Develop soft ware to diagnose the student 

• Develop assignments 

• Seek the student feed back 

• Ask the student to create his own 
assignments 

• Conduct quiz and descriptive test 
Stage 2: 

• Make interactive session to assess the 
student 

• Seek the student feed back 

• Propose alterations and development in the 
software if any 

Preferably, these procedures are conducted early 
enough at the end of each unit or chapter in the 
semester to allow weak students to take remedial 
action before being challenged. Conducting these 
stages most of the student requirements for WWW 
Based Instructional Design are satisfied which are 
proposed by wade and power [6]. 

Ill Features 

• The structure of the software especially the 
presentation, animation, colors, fonts and 
sound affects made interest in students to 
diagnose him. 

• The software structure is very interactive 
and attractive. 

• By further development of the modules in 
the soft ware, it can use for research and real 
time application problems. 



IV Requirement 

Our software package is developed in graphics using 
C - language in Turbo C++ editor. The software is 
developed on Microsoft window - 95 based system. 
The main aim is, in poor countries low economic 
colleges and lagging in new technologies can also use 
this software. The systems require a graphic card and 
program is developed for screen resolution 640X480. 

V Conclusion 

On the basis of comparative analysis the software 
packages is marked as versatile direction in 
conducting lectures, lab work and diagnose weak 
student performance. E-learning substantially 
improves and expands the learning opportunities for 
students. The modern computer 

information technologies, which are 
widely used both at educational 
programs for conducting of effective 
lecture, conducted scientific researches, 
and forming of practical and laboratory 
works 

Preferably, these procedures are conducted early 
enough at the end of each unit or chapter in the 
semester to allow weak students to take remedial 
action before being challenged. Conducting these 
stages most of the student requirements for WWW 
Based Instructional Design are satisfied 

References 

[1] Max King, Ian Kirkwood, Clive McCann, Department of 
Econometrics, Monash University," The soft(ware) approach 
to learning problems in quantitative methods" 0-7803-3173- 
7/96/$5.00 @ 1996 EEE 

[2] Vasyl Zayats, Vasyl Kogut, "Role of Information Technologies 
in Progress of Science and Education" MEMSTECH'2009, 22- 
24 April, 2009, Polyana-Svalyava (Zakarpattya), UKRAINE. 

[3] Robert S. Friedman and Fadi P. Deek, "Innovation and 
Education in the Digital Age: Reconciling the Roles of 
Pedagogy, Technology, and the Business of Learning" IEEE 
Transactions on engineering management, VOL. 50, NO. 4, 
NOVEMBER 2003, pg 403 

[4] R. Friedman and F. P. Deek, "Problem-based learning and 
problemsolving tools: synthesis and direction for distributed 
education environments," J. Interact. Learn. Res., to be 
published 

[5] F. P. Deek, M. Deek, and R. Friedman, "The virtual classroom 
experience: Viewpoints from computing and humanities," J. 
Interact. Learn. Environ., vol. 7, no. 2/3, pp. 113-136, 1999. 

[6] V. Wade and C. Power, "Evaluating the design and delivery of 
WWW based educational environments and courseware," in 
Proc. ACM-ITiCSE, Dublin, Ireland, 1998, pp. 243-248. 



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Impact of language morphologies on Search 
Engines Performance for Hindi and English 

language 



Dr. S.K Dwivedi 

Reader and Head, Computer Science Dept. 

BBAU 

Lucknow, India 

skd200@yahoo.com 

Parul Rastogi 

Research Scholar, Computer Science Dept. 

BBAU 

Lucknow, India 

parul_rastogi@yahoo.com 

Abstract - Search Engines are the basic tools of 
Information Retrieval on the web. The performance of 
the search engines are affected by various morphological 
factors of the language. The paper covers the 
comprehensive analysis and also the comparison of the 
impact of morphological factors and other language 
structure related factors (like sense ambiguity, synonyms) 
on the performance of Hindi and English language 
information retrieval on the web. 

Keywords: Hindi language morphology; English language 
morphology; web searching; morphological structure; 
precision; Hindi language Search Engines. 

I. INTRODUCTION 

Hindi language is the national language of India, 
with roughly 300 million native speakers. Another 100 
million or more use Hindi as a second language. It is 
the language of dozens of major newspapers, 
magazines, radio and television stations, and of other 
media. 

In Hindi language, nouns inflect for number and 
case. To capture their morphological variations, they 
can be categorized into various paradigms based on 
their vowel ending, gender, number and case 
information. 

Hindi language Adjectives may be inflected or 

uninflected, e.g., ^<H<£lcHl' {chamkiila} (shiny), 



'3T^5T {acchaa} (nice), "eT^T {lambaa} (long) 
inflect based on the number and case values of their 



Rajesh Kr. Goutam 

Research Scholar, Computer Science Dept. 

BBAU 

Lucknow, India 

Rajeshgoutam82@gmail.com 



head nouns while "^TcJT {sundar} (beautiful), A £nfr 

{bhaarii} (heavy) etc. do not inflect. Hindi Verbs 
inflect for the following grammatical properties 
Gender, Number, Person, Tense, Aspect and Modality 
(GNPTAM). 

The morphemes attached to a verb along with their 
corresponding analyses help identify values for 
GNPTAM features for a given verb form [1]. Hindi 
language has core distinctive structure which affects 
the results of the search queries on the web. 

English is also the popular language in India and 
across the globe. Though, the morphological structure 
of English language is not complicated as Hindi but it 
also follows some of the similar morphological 
constraints. 

Morphology is the study of the structure of words. 
The structure of words can also be studied to show 
how the meaning of a given morpheme, or its relation 
to the rest of the word, varies from one complex word 
to another. Consider how sun works in the following 
words: sunbeam, sunburn, sundial, sunflower, 
sunglasses, sunlight, sunrise, sun-spot (scientific 
sense), sun-spot (tourist sense), suntan. Inflection does 
not really yield "new" words, but alters the form of 
existing ones for specific reasons of grammar. 
Derivation, on the other hand, does lead to the creation 
of new words. 

Sometimes a word is changed in its form to show the 
internal grammar of a sentence. Examples would be 
plural forms of nouns (dog + s — ► dog-s) or past 
(imperfect) tenses of regular verbs (want + ed — ► want- 



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ed). The study of such changes is inflectional 
morphology (because the words in question are 
inflected - altered by adding a suffix). 

Other compound or complex words are made by 
adding together elements without reference to the 
internal grammar of a sentence. For example, the verb 
infect suggests a new verb disinfect (=to undo the 
action of infecting). New words are often formed by 
noun + -ize, noun + ism, or verb + able (scandalize, 
Stalinism, dispos able ). The study of such words, 
"derived" from existing words or morphemes is 
derivational morphology. Like Hindi language the 
morphological structure of the English language also 
has an impact on the performance of the search 
engines. 

II. SEARCH ENGINES SUPPORTING HINDI 
LANGUAGE 

With India emerging as a highly competitive Internet 
market, more and more people are turning towards the 
Internet. The number of internet users in India is 
increasing day by day. Now it is high time that India 
should have its own search engine leaning towards 
Indian language contents. Few of the search engines 
are already there in the market like- Google, Raftaar 
and Guruji. Google [2] is capable of searching 
information in many Indian languages successfully. 
Google is far better then other Hindi supporting search 
engines. Raftaar[3] is developed by Indicus Labs. It is 
said completely Hindi language search engine. Funded 
by Sequoia Capital, developed by students of IIT, 
Delhi Guruji [4] is also one of the popular search 
engines for Indian languages. Guruji Search can be 
described in one word - fast. The search results are 
thrown up pretty darn quickly with all the search 
results pointing towards note worthy Indian sites. 

III. FACTORS AFFECTING PERFORMANCE OF 
SEARCH ENGINES 

The information retrieval on the web in any 
language faces numerous challenges. Besides all the 
technical factors the morphological structure of the 
language is one of them which also affect the 
performance of the information retrieval on the web. 

A. Morphological factors 

Morphological factors are related to the morphology 
of any language. As discussed in previous sections the 
morphological structure of Hindi and English language 
is somewhat similar to an extent. The Hindi as well as 
English language faces the similar challenges in 



Natural Language Processing tasks, which also 
includes Information Retrieval on the web. Following 
are the challenges which both the language faces while 
information retrieval on the web. 

1) Root word of the keywords: Every language use 
some markers like (English language use s, es, ing and 

■-■, cY, 2JT in Hindi language) are used with a root 

word and new words are constructed. These new 
words are called morphological variants of the stem. 

For ex. o-ilc^i {Nadiyaan} (rivers), dlc^l {Nadiyon} 

(rivers) are morphological variant of root word 

^^{Nadi} (river). While searching on the web users 

give numerous queries which consist of words which 
are not used in their root form. 

Similarly in English language also some end markers 
are used to constuct new words as already discussed in 
this paper. For ex. Exam and Examination, Percent and 
Percentage, River and Rivers etc. 

The process of stemming of word which converts 
the word into its canonical form/ root word is 
obligatory. It is observed from th results that 
sometimes it improves the performance of the search 
engines. 

We had taken a sample set of 50 TREC queries for 
English language and 50 Hindi language queries 
collected from the various users to test the affect of the 
root word on the performance of the search engines. 

Following Table 1 and 2 are the set of randomly 
selected queries from this set. We compared the 
performance of the English and Hindi language search 
engines in the light of the root word. For Hindi 
language we had tested our queries on Google, Raftaar 
and Guruji. For English language, we had tested 
queries on Google, Altavista and Yahoo. 

Query# 1 ^WT^T # RsRff # RSjf^T {Samaaj mein 

striyon ki sthiti} (women's situation in community) 
Morphological variants of Query#l are as follows - 

#1.1 *mm # f^reit # Rsjf^r, #1.2 srara* £ ^t 

Query#2 *Trf5^F f^T^f ^T 3^=cT {Dhaarmik vivadon 

kaa ant} (end of religious disputes) 
Morphological variants of Query#2 are as follows - 

#2.1 fcrrf3fe7 facTT^f EFT 3T^T, #2.2 tnf3fa> f^T^ 3TT 

Query#3 Wt h<|ui1 {Dharm puraadon} (Religious 

Pandora) 

Morphological variants of Query#3 are as follows - 



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#3.1 Ufi TOuff, #3.2 qtf TOW 

Query#4 £TRcT % TKFzff {Bhaarat ke Rajyon} (States 

of India) 

Morphological variants of Query#4 are as follows - 

#4.1 £TRcT % ^T3^ft, #4.2 £TRcT % TK5^T 

From the results it is evident that only Google indexes 
the documents keyword in their root form. Raftaar and 
Guruji do not index in that form that is the reason 
number of document retrieved is also less in 
comparison to Google. The overall comparison of the 
results from the three search engines show that in 
general the quantity of result retrieved increased when 
the keywords are used in their root form. 

In the case of search engines the quality of results is 
more important than the quantity of the results. Table 1 
shows the comparison of the precision value of the 
three search engines. The precision value is calculated 
by taking top ten results of the search engines. On 
closely observing the result, we can say that the 
precision value in the case of Google is high in almost 
all the queries. We observed that relevancy of the 
results is also high in Google in comparison to other 
two search engines, Raftaar and Guruji which denotes 
that not only quantity but the quality of the result is 
also affected by the morphological variations of the 
keywords. 

Table I. Precision values of Google, Raftaar and Guruji 
against Hindi language sample set queries 



Que 


Results for 
(Google) 


Results for 
(Raftaar) 


Results for 
(Guruji) 




Doc. 

Retrieve 

d 


Pre. 


Doc. 
Retrieve 
d 


Pre. 


Doc. 

Retrie 

ved 


Pre. 


1.1 


1,87,000 


0.90 


1,322 


0.50 


1,87,0 
00 


0.80 


1.2 


34,200 


1.0 


3,230 


1.0 


34,20 



0.80 


2.1 


1100 


0.20 


647 


0.20 


68 





2.2 
3.1 


1000 
19,100 


0.20 
1 


1379 
2,903 


0.50 
0.5 


844 

7760 



0.4 


3.2 


24,100 


1 


2,609 


0.8 


21,07 

5 


0.5 


4.1 


6,92,000 


1 


61,691 





1,55,5 
43 


0.3 


4.2 


6,06,000 


1 


2,24,911 





6,16,1 
88 


0.3 



Query#l.l Civil service exam 1.2 Civil service 

examination 

Query#2.1 Water wastage in India 2.2 Water waste in 

India 

Query#3.1 Funding and grants institution 3.2 Funds 

and grants institution 

Query#4.1 Mercury levels in birds 4.2 Mercury level 

in birds 

Table II. Precision values of Google, Alta vista and Yahoo 
against English language sample set queries 



Q# 


Results for (Google) 


Results for (Altavista) 


Results for 
(Yahoo) 




Doc. 
Retrieved 


Precisio 
n 


Doc. 
Retrieved 


Precisio 
n 


Doc. 
Retrieved 


Precision 


1.1 


1,340,000 


0.71 


31,400,000 


0.57 


30,500,000 


0.64 


1.2 


5,590,000 


0.66 


31,400,000 


0.57 


1,600,000 


0.68 


2.1 


43000 


0.68 


698,000 


0.47 


1,660,000 


0.44 


2.2 


2,390,000 


0.52 


29,400,000 


0.64 


3,260,000 


0.68 


3.1 


62,300,000 


0.26 


25,800,000 


0.67 


25,600,000 


0.36 


3.2 


78,800,000 


0.54 


21,800,000 


0.46 


21,600,000 0.45 


4.1 


4,860,000 


0.56 


1,790,000 


0.53 


1,640,000 


0.67 


4.2 


9,180,000 


0.59 


3,480,000 


0.45 


2,850,000 


0.46 



Similarly we had taken five sample queries for English 
language also to test the affect of Root Word on the 
performance of the Search Engines. 



It is observed that all the three search engines returned 
almost same top results links with major differences in 
coverage area. Only AltaVista sometimes shows the 
same coverage area in morphological variant words. 
However Yahoo and Google did not return similar 
results in morphologically variant words. The 
relevance level of four morphological variant queries 
is calculated for the three search engines. It is found 
that Google outperforms the other two. 

2) Part of Speech (POS) Disambiguation: Part of 
Speech Disambiguation is also one of the problems 
while searching in Hindi language on the web. In 
Hindi language there are words that can behave as 
noun in some cases and the same word can behave as 
verb in some other case. It is desirable from the search 
engines to disambiguate the POS of the keyword in 
particular results. Ambiguous POS of a word will have 

a drastic affect on the results. For ex. Query '^JT 

3T?T {gaya shahar} {Gaya city) in which '^RTF is an 

ambiguous POS it is used as verb in the sense of 
'gone' and also as noun in the form of city name. All 
the three search engines (Google, Raftaar and Guruji) 
are returning maximum results in the sense of 'gone'. 
It is desired from the search engines to disambiguate 
the POS of the keywords which are ambiguous in 
nature. 

The similar problem was noticed in English 
language information retrieval on the web. For English 



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language we had tested the following queries on the 

three search engines 

Query#lCode of conduct (noun) 

Query#2 Conduct (verb) inaugural ceremony 

It is observed that only Google is able to differentiate 
between noun and verb 'conduct', whereas Altavista 
and Yahoo both of the search engines give mix results 
in both the queries which show that Yahoo and 
Altavista are not able to differentiate between noun 
and verb sense of the word 'conduct'. 

3) Phonetic Tolerance: There are numerous words 
which are phonetically equivalent but in writing they 
appear distinctively. Search engines should be capable 
of retrieving the results against any phonetically 
equivalent word of a keyword entered by the user. 
Indian languages alphabet contains many characters 
which give same sound i.e they are phonetically 
equivalent. These characters are used interchangeably 

in many modern Indian languages. e.g. ^T^T, ^fel", ^usi 

and ^o-Sl are phonetically equivalent words. 

We had observed that Google and Guruji are not 
phonetic tolerant whereas Raftaar is not able to 
identify different phonetic words. The apparent 
variations are found in the relevancy of results on the 
variation of the phonetic keywords. English language 
is not at all phonetic in nature. So such problem does 
not arise with the English language based search 
engines. 

B) Other factors 

Besides the above mentioned factors in context of 
morphological structure of Hindi language. There are 
other critical challenges for the Hindi language and 
English language searching on the web, which are as 
follows- 



Query#3 ?F3 sfe^ {Dand Baithak} (Dand Exercise) 
Query#4 3T^" [5^ll<ri {Ank Vigyan} (Numerology} 

Query#5 3^T fi^T ^ffcT {Karn Priy Sangeet} 

(Melodious Music) 

Table III. Precision of Google, Raftaar and Guruji in 

CONTEXT OF SENSE AMBIGUITY FOR HINDI LANGUAGE 



Query# 


Precision on 
Google 


Precision on 
Raftaar 


Precision on 
Guruji 


1 


0.23 


0.20 


0.10 


2 


0.40 


0.50 


0.20 


3 


0.44 


0.30 


0.30 


4 


0.50 


0.40 


0.20 


5 


0.40 


0.40 


0.10 



English language also faces the similar problem of 

ambiguous keywords like 'bank', 'bat' etc. We had 

selected the five sample queries from the complete set 

of queries which are as follows- 

Query#l built a bat house 

Query#2 Capital tours 

Query#3 Electrical current in Canada 

Query#4 Free computer class. 

Query#5 Clip art of light bulb 

Table IV. Precision of Google, Altavista and Yahoo in 

CONTEXT OF SENSE AMBIGUITY FOR ENGLISH LANGUAGE 



Query# 


Precision on 
Google 


Precision on 
Altavista 


Precision on 
Yahoo 


1 


0.56 


0.64 


0.67 


2 


0.45 


0.43 


0.56 


3 


0.71 


0.56 


0.45 


4 


0.64 


0.39 


0.57 


5 


0.46 


0.46 


0.44 



1) Ambiguous keywords: Many words are 
polysemous in nature. Finding the correct sense of the 
words in the given context is an intricate task. Various 
researchers Eric Brill [5], Argaw [6], Navigili [7] and 
Christopher Stokoe and John Tait [8] and many others 
have justified the role of Word Sense Disambiguation 
in the improvement of performance of web searching. 
Ambiguous keywords deflate the relevancy of the 
results. The example mentioned below shows this 
aspect very clearly- 

Query#l Tft^T 3uT ^TT^T {Sona aur Swasthya} 

{Sleep and Health} 

Query#2 ^T^T t£[ WT {Gulaab ki Kalam} (Rose 

Branch) 



It is quiet obvious from the results mentioned in Table 
3 and 4 that in both languages, Search Engines are not 
capable enough to cope up with this problem. The 
results show the low precision values which justify that 
the performance of the search engines is affected by 
the sense ambiguity. 

2) Word Synonyms: Every language has words and 
its synonyms. It is observed while working on English 
and Hindi language search engines that any word can 
express a myriad of implications, connotations, and 
attitudes in addition to its basic 'dictionary' meaning. 
Choosing the right word can be difficult for people, as 
well as for the Information Retrieval System. It is seen 
that most of the times when we alter the keywords with 



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their synonyms the performance of the search engines 
varies. 

Query #1.1 3TRSePT £ RTRRfl {Arakshan se faida} 

(Benefits of reservation) 

Query #1.2 3TTTSHW £ oTT3T {Arakshan se laabh} 

(Benefits of reservation) 

Query #2.1 £TRcT % TK5^T-{Bhaarat ke rajya} (States 

of India) 

Query #2.2 £TRcT % ^f {Bhaarat ke Pradesh} 

(States of India) 

Query #3.1 ^lM" oft # ^Tc^T {Gaandhi ji ki Mrityu} 

(Death of Gandhi ji) 

Query #3.2 ^fptfr oft ^T f^TtR" {Gandhi ji ka Nidhan} 

{Death of Gandhi ji} 

Query # 4.1 q^TT 3T?lfeT ^T^T {Pehla Antriksh 

Yaan} 

(First Space ship) 

Query #4.2 W^OT 3f?lfeT 2JR- (Pratham Antriksh 

Yaan) (First Space ship) 

Table V. Precision of Google, Raftaar and Guruji in 

CONTEXT OF SYNONYM PROBLEM FOR HINDI LANGUAGE 



Query 


Results for 
Google 


Results for 
Raftaar 


Results for 
Guruji 




Doc. 


Pre. 


Doc. 


Pre. 


Doc. 


Pre. 


1.1 


3720 


.70 


4489 


.50 


24 


.30 


1.2 


189,000 


.90 


15,408 


.80 


1625 


.90 


2.1 


3,590,00 



.70 


319,903 


.10 


29,03 
8 


.40 


2.2 


2,670,00 



.80 


309,138 


.10 


37,75 



.30 


3.1 


44,000 


.60 


12,844 





516 


.30 


3.2 


22,200 


.10 


18,455 





517 


.10 


4.1 


27,300 


.80 


2349 


.40 


6285 


.70 


4.2 


13,000 


.20 


1314 





308 


.60 



The variation in results is found in Query#l and 
Query#2 when alternative synonyms are used. In 

Query#l c eTT§T' in place of 'STO^T and in Query#2 

iJ 3%%r in place of 'THr^F changed the result set. It is 

justified that replacing some words with their 
synonyms some times improves the results against the 
query in any language. 

The problem of synonyms lies in every language. 
English language also faces the similar problem. 
Changing the keywords of the query with its synonyms 
in general vary the relevancy level of the results. From 
the complete set of 50 queries 5 queries are used to 



justify the impact of varying synonyms on the web 

search results in English language. 

Query# 1 . 1 bed sharing with children 

Query# 1.2 bed sharing with kids 

Query# 2.1 school bus safety 

Query# 2.2 school bus security 

Query# 3.1 aircraft safety act of 2000 

Query# 3.2 aircraft protection act of 2000 

Query # 4.1 Freedom of information act forms. 

Query# 4.2 Liberty of information act forms. 

Table VI. Precision of Google, Raftaar and Guruji in 

CONTEXT OF SYNONYM PROBLEM FOR ENGLISH LANGUAGE 



Qu 


Results for 


Results for 


Results for 


ery 

# 


(Google) 


(Altavista) 


(Yahoo) 








Doc. 




Doc. 






Doc. 


Prec 


Retrieve 


Prec 


Retriev 


Prec 




Retrieved 


ision 


d 


ision 


ed 


ision 




1,070,000 




82,200,0 




77,300, 




1.1 




0.44 


00 


0.65 


000 


0.47 




1,180,000 




68,500,0 




62,900, 




1.2 




0.56 


00 


0.48 


000 


0.43 




9,460,000 




47,400,0 




48,700, 




2.1 




0.67 


00 


0.45 


000 


0.49 




12,500,00 




51,400,0 




48,000, 




2.2 





0.68 


00 


0.57 


000 


0.67 




231,000 




6,480,00 




39,200 




3.1 




0.68 





0.35 




0.68 




420,000 




6,480,00 




77,300 




3.2 


20,600,00 


0.66 



62,900,0 


0.36 


725,00 


0.65 


4.1 



908,000 


0.47 


00 
19,200,0 


0.48 



253000 


0.57 


4.2 




0.39 


00 


0.35 




0.47 



From the results mentioned in Table V and VI it is 
evident that the variation of query terms with their 
synonyms varies the precision level of the results. 

IV. DISCUSSION 

We have done comprehensive comparison of the 
performance of the English language and Hindi 
language based search engines in respect to their 
morphological structure and also other factors. On 
comparing the results of the query sample set it is 
concluded that when the query is given in its root form 
it returns into the maximization of results in Hindi 
language but only sometimes in English language. But 
in both the cases it is crystal clear from the results in 
Table 7 that the precision values are better when the 
key terms of the queries are in their root form. 

From the results it is quiet evident that Google 
indexes the keyword in its root form for Hindi 
language but not for the English language. It is capable 
of listing the documents consisting of all 



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morphological variants of the keywords which justifies 
that the Google do not require stemmer for Hindi 
language web information retrieval. The other two 
search engines Raftaar and Guruji are not listing all the 
morphological variants of the query and hence they 
entail to have stemmer. For English language Web IR 
Altavista and Yahoo also show some improvement of 
results when query terms are used in their root forms. 
The results in Table VII justify that we do require 
converting the keywords in their root form to improve 
the relevancy of the results. 

All the three search engines are not able to 
disambiguate the POS as mentioned in section 3. For 
English language Web IR only Google is capable of 
disambiguating POS whereas Altavista and Yahoo are 
not able to do that. Only Raftaar is upto some extent 
Phonetic tolerant. Google and Guruji are not phonetic 
tolerant for Hindi language. English language does not 
face the problem of phonetic terms. 

None of the three (Google, Raftaar and Guruji) are 
attuned with ambiguity problem. Even English 
language search engines are also not attuned with 
ambiguity problem. On comparing the performance of 
the English and Hindi language search engines we 
found that precision values of English language is 
somewhat better than the Hindi language though the 
affect of ambiguity is quite visible on the performance 
of the Hindi as well as English language search 
engines. The Search Engines performance is degraded 
because of the sense ambiguity problem in Hindi as 
well as English language. 

Sense / Synonym management is one of the 
challenges mentioned. It is marked that all the three 
search engines do not implement sense disambiguation 
or synonym management. Sense disambiguation 
improves the relevancy of results in web searching, as 
various researchers have successfully justified the 
application of WSD and synonym management in web 
searching. 

It is apparent from the results that relevancy of the 
results retrieved by the search engines is dependent on 
the morphological structure and also, senses and 
synonyms in English as well as Hindi language. 

An overall comparison we conclude that the 
performance of the English and Hindi language search 



engines affected by their morphology to some extent. 
Since the English language search engines are grown- 
up enough so the percentage of this affect is less in 
English language search engines as compared to Hindi 
language search engines. 

V. CONCLUSION 

We have compared the performance of the two 
language search engines i.e. English and Hindi in the 
light of their morphological structure and other factors 
also. 

Our results conclude that the performance of the 
search engines is quiet affected by the morphological 
issues as well as sense ambiguity and synonym 
problems. This affect is much obvious in the Hindi 
language search engines in comparison to the English 
language. 

The morphological structure of Hindi language is 
more critical in comparison to the English language. 
This is the reason the performance of the Hindi 
language is more affected by such issues. 



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Table VII. Precision of Google, Raftaar and Guruji in context of Synonym Problem for English Language 



Parameters 


Hindi language Search Engines 
Performance 


English language Search 
Engines Performance 




Google 


Raftaar 


Guruji 


Google Altavista 


Yahoo 


Root Word 


With Root Word 


0.80 


0.57 


0.40 


0.59 


0.53 


0.55 


Without Root 
Word 


0.77 


0.30 


0.37 


0.54 


0.56 


0.53 


Sense Ambiguity 


0.39 


0.36 


0.18 


0.56 


0.50 


0.54 


Synonym 
Management 


Variation 

in 

Precision 


Variation 

in 

Precision 


Variation 

in 

Precision 


Variation in 
Precision 


Variation in 
Precision 


Variation in 
Precision 


POS 
Disambiguation 


Do not 
support 


Do not 
support 


Do not 
support 


Support to 
some extent 


Do not 
Support 


Do not 
Support 


Phonetic 
Tolerance 


Not 

Phonetic 

Tolerant 


Not 

Phonetic 

Tolerant 


To some 
extent 
Phonetic 
Tolerant 


Not a phonetic Language 



REFERENCES 

[1] P. Bhattacharya, S. Singh, K. Gupta, and M. Srivastava, 

"Morphological richness offsets resource demand- experiences in 

constructing a POS tagger for Hindi" in Proceedings of COLING, 

06, Sydney, Australia, pp.- 779-786, 2006 

[2] http://www.google.com 

[3] http://www.raftaar.com 

[4] http://www.guruji.com 

[5] E. Bril and S. Vassilvitskii, "Using WebGraph Distance for 

Relevance Feedback in Web Search" in Proceedings of SIGIR'06, 

Seattle, Washington, USA, pp. -147-153, 2006 



[6] A. A. Argaw, "Amharic-English Information Retrieval with 
Pseudo Relevance Feedback", in Proceedings of 8th Workshop of 
the Cross-Language Evaluation Forum, CLEF 2007, Budapest, 
Hungary, pp. 119-126,2007 

[7] R. Navigili and P. Velardi, "Structural Semantic 
Interconnection: a knowledge-based approach to Word Sense 
Disambiguation", in Journal of Pattern Analysis and Machine 
Intelligence, Volume 27, Issue 7, pp. 1075 - 1086, July 2005 
[8] C. Stokoe and J. Tait, "Towards a Sense Based Document 
Representation for Internet Information Retrieval", in Proceedings 
of SIGIR'03, July 28- August 1, Toronto, Canada, pp. 791-795, 
2003 



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Comparison of Traffic in Manhattan Street 

Network in NS2 



Ravinder Bahl (Author 1 ) 

Information and Technology 

M.M.E.C 

Muallana, Ambala, Haryana, India 

ravindra_ibm@yahoo . com 



Rakesh Kumar (Author ) 

Information and Technology 

M.M.E.C 

Muallana, Ambala, Haryana, India 

raakeshdhiman@gmail.com 



Rakesh Sambyal(Author ) 

Information and Technology 

MBS College of Engineering and Technology 

Babliana, Jammu, Jammu and Kashmir,India 

rakeshsambyal@rediffmail.com 



Abstract — The paper presents the Comparison analysis of traffic 
in Manhattan Street Network (MSN). The behaviour of 
Manhattan Street Network for constant bit rate (CBR) and 
exponential traffic sources is demonstrated. The results are 
produced using NS2 simulator. It is concluded that the 
performance in multipath networks like MSN can be improved 
by taking account of appropriate buffering for each traffic source 
at the link node. 

Keywords — Comparison Analysis, Traffic, Constant bit rate 
(CBR), Exponential, MSN, Buffering. 



I. Introduction 

The selection of buffer is very important as it helps in 
reducing the congestion and drop in packets .It helps in 
balancing the load across the multipath and multihop 
networks like Manhattan Street Network (MSN). The traffic 
sources like constant bit rate (CBR) and exponential offer 
different type of load in the network. In the large networks like 
Manhattan Street Network the traffic is routed form two input 
links to two outgoing links having equal bandwidth and delay. 
The packet is stored in buffers upon arrival and is deflected on 
the other. It cannot be accommodated at a later stage even if 
the buffer allows admission to new arrivals at that stage. In 
voice network and private line data networks the capacity 
planning is very simple and straight forward with problem of 
congestion and packet drops. For the selection of buffer 
capacities, the growing traffic volume is to be calculated. As 
the traffic increases so is the demand for more bandwidth. The 
appropriate size and type buffer selection can reduce the 
growing demand for more bandwidth. In this paper, attempt 
has been made make provision for reducing congestion and 
packet drops in different traffic sources like constant bit rate 
(CBR) and exponential. In order to reduce the congestion and 
packet drop , the drop tail queue is provided at each input node 
which also help in reducing the deflections. 



Further regardless of the destination, the buffering structure is 
so designed to store the packets and deflection occurs only 
when all the buffer slots are full. The distance vector 
routing(RIP) and link state (OSPF) play a very important role 
in today's internet [l].The simulation comparison of buffers in 
OSPF and RIP algorithm conclude that the link state routing 
performs better than distance vector routing in case of large 
networks like Manhattan Street Network topology where large 
volume of traffic flows form source to destination. 

II. Background of routing algorithm 

In case there are more than two paths from source to the 
destination. The path selected between source and destination 
represents the outgoing link to be used. The routing algorithms 
based on cost metrics [5] helps in calculating the path between 
the nodes. The cost is defined in terms of number of hops or 
bandwidth, number of links, distance. This depends upon the 
metric supported by different of buffer capacities. Many 
different cost metrics [5] can be used to judge the shortest path 
between the source and destination. 

III. Concept of multipath routing algorithm 

In multipath networks where data transactions in large volume 
take place from source to destination, the path with the 
optimum cost is to be selected to route the data. The selection 
of improper path leads to over utilization of the network 
resources resulting increasing delays and congestion, core 
networks where there is a huge amount of data transactions 
and there are more than one equal cost route possible from a 
source node to destination node with large volume of traffic, 
the multipath routing algorithm [6] [7] [9] may be used, which 
helps in improving the available resources utilization and 
helps in reducing congestion and packet drops and thus helps 
in shaping the traffic between equal cost multi paths. The links 
utilization [2] can be improved. By having improper queues 
which in turn implies increasing delays, so sort of trade off is 
required for selection of appropriate queue. The simulation has 



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proved that appropriate size queue for particular traffic source 
results in better resource utilization .Results help in deciding 
the size of queue at the link for better performance. 
The selection of appropriate size queue avoids congestion at a 
particular node including cross-traffic as given below. 

(A) Departure of traffic from a node should be equal to the 
traffic arriving at that node. 

(B) It is essential in case of multipath networks that the load 
should be balanced in such a way so that none of the outgoing 
link of a router is over utilized. 

(C) The appropriate queue with optimum storage capacity may 
be maintained at the bottleneck links for better performance. 

IV. Manhattan Street network (msn) topology 

(A) The topology used in the paper for the purpose of 
demonstration has 16 nodes and 32 links of equal bandwidth 
and delay. 

(B) The two traffic sources, constant bit rate and 
exponential are used at the link node one at a time. 



TABLE I : Showing Simulation Result with queue size 

OF NO PACKET. 




Fig.(l) : Experimental Topology (MxN) , Where (M=4,N=4) 

V. Simulation results 

The simulation study was performed and analysis were drawn 
using Network Simulator (NS2) [8]. The Simulation results are 
evaluated for in different buffering capacities required for each 
traffic source using topology of Fig. (1) on case to case basis. 



Packet 
Size(bytes) 


Traffic Type 


Total Packet 


Non 

Dropped 

Packets 


Dropped 
Packets 


64 


CBR 


2002 


2002 





64 


Exponential 


3110 


3110 






TABLE I : Showing Simulation Result with queue size 

OF 1 PACKET. 



Packet 
Size(bytes) 


Traffic Type 


Total Packet 


Non 

Dropped 

Packets 


Dropped 
Packets 


64 


CBR 


2002 





2002 


64 


Exponential 


3110 





3110 



TABLE III: Showing Simulation Result with queue 

SIZE OF 2 PACKETS. 



Packet 
Size(bytes) 


Traffic Type 


Total 
Packet 


Non 

Dropped 

Packets 


Dropped Packets 


64 


CBR 


2002 


2002 





64 


Exponential 


3110 


3110 






All links set to type simplex having bandwidth (1Mbps), delay 
(10ms) the offered load was observed between nl to nl6 with 
two constant bit rate and two exponential traffic sources 
starting at nl and nl6 as destination. The drop tail queue was 
attached between nl and nl6. 



VI. Bottleneck link cases 

The traffic from node nl to nl6 was observed where the link 
bandwidth was set to 1Mbps, delay of 10 ms, and packet size 
of 64 bytes. Initially two constant bite rate traffic sources and 
later two exponential traffic sources with interval of 0.005 
seconds and drop tail queue was introduced at a time. Initially 
queue size was set to no packet and later was changed to the 
capacity of more packets and performance was analyzed. The 
simulation was run for 10 seconds and traffic was introduced 
for 5 Seconds and the Following Results were obtained. 



VII. 



PERFORMANCE GRAPHS 



The performance graph for bottleneck link, when one drop 
tail queue with FIFO (First in First out) discipline is 
maintained at the bottleneck link with two different traffic 
sources as shown in Fig.(2). , Fig. (3), Fig. (4) and Fig. (5) 
Respectively. 



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I 

§ 0.6 

JE 
5 

0.2 





*CBR Traffic 1 
>CBR Traffic 2 



1 4 7 10 13 16 19 22 
Time 



Fig. (2): CBR Traffic in Packets with Drop Tail Queue of size 



1200 

1000 




■CBR Traffic 
1 

-CBR Traffic 
2 



14 7 10131619 
Time 



Fig. (3): CBR Traffic in Packets with Drop Tail Queue of size 2 











Chart Title 


— **Emxwe? rati ill raNic 1 

H^-C*portrnLijlTr,iirit 2 


0.9 - 
Q.B 
n 7 
































J£ D.6 




















1 S 4 












■■1 =■ 










•1 1 










h 1 










p 












1 


3 5 


7 9 


11 is 

Tune 


1* 1? 14 21 



Fig. (4): Exponential Traffic in Packets with Drop Tail Queue of size 









■Vh 


*»**»»+*♦» 










3 [ ™ 














| "KJD 












—^-Li[*jni:rHifll Trunk 1 




j vt 










-^OpOilm-Hi^lTrfiFpt 2 




























D 






12 3 3 5 6 7 4 5 E01113JSia]516171BI5M2I 
Tift* 



Fig. (5): Exponential Traffic in Packets with Drop Tail Queue of size 2 

The simulation result helps in deciding the type of buffer at 
the bottleneck links. Case study of buffer types for a 
particular case is explained. The decision can be made based 
on the performance graph that the appropriate buffer is 
implemented at the bottleneck links for a particular case such 
that the congestion in the network may be reduced and 
performance may be improved. 

VIII. Conclusion 



The demonstration of buffer selection at bottleneck links in the 
Manhattan Street Network Topology is done. 

The paper demonstrated with different types of buffer , packet 
size of 64 bytes and two constant bit rate (CBR) traffic sources 
starting form node nl for destination nl6 the following effects 

Case (1) Simulation of multipath network topology (MSN) 

with drop tail node queue having storage capacity of packets 

with two constant bit rate traffic Sources led to total packet 

drop. 

Case (2) Simulation of multipath network topology (MSN) 

with drop tail node queue having storage capacity of packets 

with two exponential traffic Sources led to total packet drop. 

Case (3) Simulation of multipath network topology (MSN) 

with drop tail node queue having storage capacity of one 

packet with two constant bit rate traffic Sources led to total 

packet drop 

Case (4) Simulation of multipath network topology (MSN) 

with drop tail node queue having storage capacity of one 

packet with two constant bit rate traffic Sources led to total 

packet drop 

Case (5) Further it was concluded that Simulation of multipath 

network topology (MSN) with drop tail node queue having 

storage capacity of two packet with either two constant bit rate 

or exponential traffic Sources led to no packet drop 



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IX. References 



[1] Routing Basics,http:// www.cisco.com/ univercd / cc / td /doc/cisintwk/ 

itodoc/ routing.htm. 
[2] James Irvine, David Hade, "Data Communication and Networks", John 

Wiley & Sons Ltd.,New York, USA,2002. 
[3] William Stallings ,"Data and Computer Communications", PHI Pvt. Ltd. 

N.Delhi, 7 th Edition,2003. 
[4] Alberto Leon-Garcia, Indra Widjaj a, ["Communication Networks, 

Fundamental Concepts and Key Architectures",Tata McGraw-Hill 

Publishing Company Ltd.,N.Delhi,2nd Edition, 2005. 
[5] Brian Hill , "The Complete Reference,CISCO", Tata McGraw-Hill 

Publishing Company Ltd., N.Delhi, 3rd reprint 2004. 
[6] Johnny Chen, Peter Druschel, Devika Subramanian, 

"An Efficient Multipath Forwarding Method",Proceedings of IEEE 

INFOCOM , San Francisco, CA,March 1998. 
[7] Israel Cidon, Raphael Rom, Yuval Shavitt, "Analysis of Multi-path 

Routing", IEEE/ACM Transanctions on Networking, 

Vol.7,No.6,Dec.l999. 
[8] The Network Simulator ns-2, http://www.isi.edu/nsnam/ns/ns- 

documentation. 
[9] Ivan Gojmerac, Thomas Ziegler, Fabio Ricciato, Peter Reichl, 

"Adaptive Multipath Routing for Dynamic Traffic Engineering", IEEE 

GLOBECOM-2003,http:// userver.ftw.at /-reichl /publications 

/GLOBECOM03.pdf. 



1 82 http://sites.google.com/site/ijcsis/ 

ISSN 1947-5500 



An Evolving Order Regularized Affine 
Projection Algorithm, Suitable For Echo 



Cancellation 



Shifali Srivastava 
Electronics Deptt. 
JUT, 

Noida, India 
shifalihbti2QQ4@gmail.com 



M.C. Srivastava 
Electronics Deptt. 
JUT 

Noida, India 
m.c.srivastava@jiit.co.in 



Abstract — In this paper, a regularized Affine Projection algo- 
rithm with Evolving Order is proposed. This algorithm auto- 
matically determines its projection order, derived in the con- 
text of acoustic echo cancellation (AEC). The simulation results 
indicate that the proposed algorithm yields better performance 
with small steady state error as compared to existing evolving 
order affine projection algorithm (APA) and has fast conver- 
gence speed. 

Keywords — Acoustic echo cancellation (AEC), affine projec- 
tion algorithm (APA), evolving order affine projection algorithm 
(EO-APA), Evolving order regularized affine projection algo- 
rithm (EO-RAPA), double talk(DT),echo path change(EPC). 



I. INTRODUCTION 

In acoustic echo cancellation (AEC) contexts the basic 
approach is to build a model of the impulse response of the 
echo path using an adaptive filter, which provides replica of 
the echo at its output [1]. The adaptive filter output is sub- 
tracted from the microphone signal to cancel the echo. Sev- 
eral challenges are associated with AEC applications. 
Firstly, the echo path can be extremely long and it may ra- 
pidly change. Secondly, the background noise that appears 
at the near-end side can be strong and non- stationary in na- 
ture. Further, the involved signals (i.e., speech) are non- 
stationary and highly correlated. 

For echo cancellation several adaptive algorithms [1], 
[2] have been applied. The normalized least-mean square 
(NLMS) algorithm and the affine projection algorithm 
(APA) are preferred due to their simplicity and robustness. 
The affine projection algorithm (APA) [4], [5] updates the 
weights based on the last input vectors. The convergence 
speed of APA for correlated input signal is improved by 
employing an updating-projection scheme of an adaptive 
filter on a P-dimensional data-related subspace, but the con- 
vergence speed of APA decreases, in the presence of noise. 

Recently, EO-APA has been proposed to improve per- 
formance in the presence of noise, with fast convergence 
speed and small steady state error by varying the number of 
input vectors [7]. An evolutionary method is employed in 
EO-APA to determine necessary number of input vectors. In 
this algorithm order of vector increases or decreases from 
the previous one by comparing the output error with the 



threshold involving the information of the steady-state 
mean-squared error (MSE) [8]. 

The convergence speed of EO-APA gets degraded during 
unvoiced speech signal and/or during silences when signal 
value is either zero or close to zero. This paper proposes 
solution of such problems by employing an evolving order 
regularized Affine Projection Algorithm (EO-RAPA). In the 
proposed algorithm, regularization is obtained by adding a 
regularization factor matrix prior to taking inverse of the 
correlation matrix. With the suitable choice of regularization 
factor, the performance of the algorithm can be improved 
with decreased computational complexity. 

A major concern associated with the behavior of the 
algorithm during double-talk, that may affect the overall 
performance of proposed algorithm, is also addressed in the 
proposed scheme. The standard procedure can be used for 
double talk detection in order to slow down or completely 
halt the adaptation process during double-talk periods. 
Several algorithms have been proposed for detection of 
double talk (DT) [9]— [11]. The simplest double talk detec- 
tion algorithm is the well-known Geigel DTD [11], which 
provides a low-complexity solution. Since it is not efficient 
to distinguish between echo path change and double talk, a 
modified version of it is proposed in this paper. 

This paper is organized as follows. Section II describes 
evolving order APA (EO-APA). In section III, we propose a 
regularized version of evolving order APA (EO-RAPA) for 
AEC and performance improvement during double talk. The 
experimental results which illustrate the convergence per- 
formance of the proposed algorithm are discussed in section 
IV. Finally, conclusions are presented in Section V. 



II. EO-APA 



fir end signal s(k) 



-:*) 



Echo 
I fljiceller 
Structure 



Loudspeake 
(Nonlinear) 

ze: 



Room 
Response 



.#) 



y(k) 



■B 



Microphone 
Response 



X 



echo of i(k) and/or 
background a;) J or 
near end speech 



Desired signal d(k) 
Figure 1 Acoustic echo canceller structure 



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A general acoustic echo canceller structure is depicted in 
Fig. l.The aim of this structure is to model the acoustic echo 
path (i.e. room impulse response) using an adaptive filter. 
As shown in figure the far end signal x(k), on being 
processed by the room impulse response, produces echo 
signal. This echo signal is picked up by the microphone 
and/or the near end signal v(k) and/or background noise 
n(k) to produce a desired signal d(k). The far end signal that 
also acts as the input to the adaptive filter produces a output 
y(k) , the replica of echo signal. 

The error signal e (k) = d k -y(k) , is used for weight adapta- 
tion of the filter. With the far end signal denoted by X k in the 
matrix form, the desired d k may be expressed as: 

d k = W T opt X k + n k (1) 



where X k = [x k , x k h . . ., x k L+1 ] ,the subscript k 
represents the time index, W opt is an unknown L x 1 weight 
adaptation column vector and n k is a zero-mean Gaussian 

2 

independent sequence with variance of <J n . 

An estimate W* for W opt at iteration k may be computed as 
follows [4] by using an affme projection algorithm (APA): 



H , 



T //,-l 



W *= W *-1 + W(U*U£) 'e t 



(2) 



where ju is a constant step size .The error signal e k may 
therefore be expressed as: 

,T (3) 



e k= d k 



U * W £ 



where 

d k = [d k , d k _!, ... , d k . P+1 ] T (4) 

The matrix in (3) U k is the collection of the P most recent 
input vectors \X k X k . h ••• , X k . P+1 ] T . 
The order of the APA is defined by the projection order P, 
,the number of the input vectors used to determine U k , that 
should be less than or equal to filter length L [l]-[3]. In EO- 
APA the projection order P, at any iteration that varies in 
accordance with adaptation, may be represented as [7] : 

p k=K p k-v& (5) 

Since the number of input vectors at any iteration depends 
on the output error and the previous number of input vectors, 
the projection order adjusts itself if error exceeds a particular 
threshold that can be determined by approximating steady- 
state MSE. 

Following Shin and Sayed [8], for each iteration the thre- 
shold error may be written as: 

(6) 



*7i 



^{//0P-l) + 2} 
2-jU 



8{P) 



If square of the error signal e (k) is smaller than^(P ) , 

k - 1 

projection order P k at the k th iteration in EO-APA should be 
reduced by one from P k .j (its previous value), for smaller 
steady state error. Whereas, P k should be increased by one 

from P k _i when e(k) is larger than, s(P k _ l + 1) for faster 
convergence speed. Therefore, the upper and lower thre- 
sholds n and 6 k at k th iteration respectively may be ex- 

pressed for EO-APA can be expressed as: 



n k =s(P k _ l+ D- 



4^ p k-\ + V 

2-ju 



and 



*k = « p k-i> = 



2- M 



(7) 



(8) 



With these thresholds bound the projection order at any 

iteration may be determined as: 

2 
MifiP kl + 1, P max } if t^ < e (k) 

2 



4-i 



(9) 



ifO k <ep)<? lk 

Max[P k4 -lA} ifel<0 k 

The upper threshold controls the projection order of the EO- 
APA to track the increased output error due to variation in 
environment. Similarly the lower threshold acts as a switch- 
ing point to decrease the projection order. Thus EO-APA is 
expected to provide fast convergence speed with decrease in 
steady-state error. 

III. EO-RAPA 

H. Rey, L. Rey Vega, S. Tressens, and J. Benesty in 
[13], employed a variable explicit regularization factor in 
their work on APA. They suggested an optimal value of re- 
gularization factor ( S ) chosen such that minimizing differ- 
ence of weight error vector of the consecutive iterations re- 
sulted in lower steady state error. Such a choice maximizes 
speed of convergence and minimizes steady-state mismatch. 
Under simplifying assumption they define S 



as: 



P.g 2 .g 2 .L 



(10) 



-Pg a 



In the proposed work a regularization factor has been 
added in the EO-APA. During unvoiced speech or during 
silences, the signal level is either very low or zero and there- 
fore inverse of (U^U^) gets ill conditioned in weight upda- 
tion in (2). Further, with the increase in the projection order 
of the EO-APA, computational effort for determining in- 
verse of the matrix increases. To avoid these problems a 
regularization factor is added before taking inverse in the 
proposed algorithm. The regularization factor also helps in 
suppressing the effect of noise, which may be the back- 
ground noise or/and the near-end speech corrupting the out- 
put of the echo path. The proposed algorithm referred as 
evolving order regularized APA (EO-RAPA), may be ex- 
pressed as: 

W, = W M +/ Aj£, (U, 4 uf 4 +<5I)" 1 e, 4 (") 

The identity matrix 1= P k *P k and £is the regularization 
factor. 



Performance improvement during Double Talk: 

When the speech signal v (k) is zero and the near-end 
noise n (k) is assumed to be insignificant, weights of the 
adaptive filter would converge to successfully cancel the 
echo, and successfully cancel the echo. However, when both 



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v(k) and x(k) are not zero, that is in double-talk (DT) situa- 
tion, the near end speech v(k), acts as an uncorrected noise 
to the adaptive algorithm, and may allow excessive un- 
cancelled echo to pass. Solution to this problem is to slow 
down or completely stop the filter adaptation when the pres- 
ence of the near-end speech is detected. 

A simple approach to detect DT is the well-known Gei- 
gel algorithm [11]. The Geigel algorithm compares the mag- 
nitude of the near-end received signal d (k) with the maxi- 
mum magnitude of L most recent samples of the far-end 
signal x(k), where L is the adaptive filter's length. The Gei- 
gel algorithm computes its detection variable and makes 
decision. If detection variable is larger than the threshold , 
DT is declared otherwise not. However, for an AEC envi- 
ronment, the echo path characteristics are time varying. 
Therefore, for the time-varying echo path, DT can be falsely 
detected by the Geigel algorithm when a change of the echo 
path occurs. As a result, the adaptive filter stops updating 
the coefficients when the coefficient update is actually 
needed. 

In the proposed algorithm EO-RAPA a detection scheme 
is presented that detects and distinguishes between echo path 
change and double talk. Simulation results show that this 
detection method provides adequately reliable performance 
with lower complexity. This method is based on computa- 



tion of detection variable £ at any iteration: 
mean power of microphone signal d(k) 



# 



mean power of far end signal X(k) 



(13) 



If £ > threshold T h and at the same time projection order is 

above the certain value say P th , double talk is detected. Since 
projection order is directly related to the output error that 
corresponds to the cross correlation between microphone 
signal and far-end signal. Thus, our detection method is 
power and correlation based method. 

In the case of EPC condition, projection order is above 
a certain value P th but £ < threshold T h . We therefore employ 
two thresholds one on £ and one on projection order, to 

reduce the probability of false detection of DT or EPC con- 
ditions. Flow chart of DT detection scheme is shown in 
Fig. 2. 




Monitor £, 



AND Function 




IV. SIMULATION RESULTS 

The performance of the proposed algorithm is verified by 

carrying out experiments in context of echo cancellation 

with speech input sampled at 8 KHz and echo path length of 

512. 

ERLE, an important parameter to measure of convergence 

speed and misadjustment that may be expressed in dB by 

(13), is employed for performance evaluation. 



ERLE = 10* log 



E 


\ a -\ 


E 


kl 



(13) 



is 



where o d is the power of the microphone signal and 

the power of the residual echo. 

In Fig. 3, by ensemble averaging over 20 independent 
speech samples, ERLE plot is obtained for proposed algo- 
rithm with variable regularization factor, EO- APA and clas- 
sical APA for different projection orders {P=% and 16), 
SNR=30 dB. As shown by simulation results the perfor- 
mance of proposed algorithm is better in terms of conver- 
gence speed and low steady state error. 



o 

-5 
-10 

m 
lu -20 

m -25 

-30 
-35 
-40 



— EO-RAPA 
EO-APA 

— APA(P=16) 
APA(P=8) 




1000 2000 3000 4000 5000 6000 7000 8000 9000 
No. of iterations 



Figure 3 ERLE plot of proposed algorithm, EO- RAPA and classical APA (echo 
path length =512, speech input sampled at 8000 Hz, step-size=0.5) 



Fig. 4 Shows ERLE plot for different values of fixed regula- 
rized factor ( 8 ). Simulation results show the practical justi- 
fication that as 8 increases, steady state mismatch reduces 

2 
but at the cost of lower convergence speed. For 8 =0.5* <r , 

x 

convergence speed is faster but steady state error increases. 

2 
On the other hand for 8 = 500 * a slower convergence 

x 

speed is obtained with lower steady state error. 



Double Talk 
Detected 



Figure 2 Flow chart of DT detection scheme 



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-5 

-10 
-15 r 

I -20 | 
-25 I 
-30 

-35 [W 
40 



— del=0.5*sigma 
del=5*sigma 

— del=20*sigma 

— del=50*sigma 
del=500*sigma 



1.5 2 2.5 

no of iteration 



3.5 
< 10 4 



Figure 4 ERLE plot of proposed algorithm with different values of fixed regularization 
factor (where del is regularization factor and sigma is mean power of far end signal), 
SNR=30dB 

Double talk scenario is depicted in Fig. 5, ERLE plot 
of proposed algorithm when DT occurs from 19,000 to 
21,000 with proposed double talk detection (DTD) scheme 
and without DTD scheme. Simulation results indicate that 
deterioration in performance during double talk (DT) is im- 
proved by employing proposed DTD scheme. 




0.5 



1.5 



2 



15 



3 



20- 


I 

Ml 




1 

J I Lull 




1 




0.5 



i 


1.5 


2 
o. of iterations 




2.5 


3 3.5 

xio' 


0- 
0- 

ii 1 [ 1. 


i ..1 „K 


|l 


1 

JijL. 




,.L L 


J i ., 


0.5 


1 


15 


2 




25 


3 3 

x10 4 


1 ! 

L * 1 d * 


< I 


f I" 




HI 1" — 









35 



no. of iterations ^ 

Figure 6 Microphone signal, Far end signal, projection Order, Detection variable signal (Zeta) 

and error plot of proposed algorithm when EPC occurs at iteration 10,000 and DT occurs 

between 17,000 tol9,000 with DTD scheme, SNR=20dB 

Fig 7 shows the performance of proposed algorithm by em- 
ploying DT detection scheme when EPC and DT occurs at 
different iterations. 




^-400 

ESoo 



1.5 2 

no. of iterations 



JO 




no. of iterations 


xlO 


- 


— without DTD scheme 
— with DTD scheme 




J20 


/ 






| 


- 


]-40 


- 





0.5 



2 



2.5 



3 



1 1.5 

no.of iterations x1Q 4 

Figure 5 Microphone signal, far end signal, projection Order, Detection variable signal and 

ERLE plot of proposed algorithm when DT occurs between 19,000 to 21,000 



Fig. 6 shows plots of different signals and it can easily seen 
from simulation results that proposed DTD scheme detect 
and distinguish EPC and DT. 



O 0.5 1 1.5 2 2.5 3 3.5 

no of iterations x -, Q 4 

Figure 7 ERLE Plot of the proposed algorithm with DTD scheme when EPC occurs at itera- 
tion 10,000 and DT occurs between iterations 17,000 to 19,000, SNR=20dB 



V. CONCLUSIONS 

In this paper an evolving order regularized affine pro- 
jection algorithm (EO-RAPA) has been evolved that is suit- 
able for AEC applications. The performance of the proposed 
algorithm is verified by carrying out experiments in context 
of echo cancellation with speech input sampled at 8KHz. 
Simulation results indicate that proposed algorithm has fast- 
er convergence speed and lower steady state mismatch com- 
pared to existing EO-APA. Further the proposed algorithm 
provides an improvement in the performance during double 
talk by employing a new DTD scheme. 



REFERENCES: 

1. S. Haykin, Adaptive Filter Theory, 4th ed. Upper 
Saddle River, NJ: Prentice Hall, 2002. 

2. A.H.Sayed, Fundamentals of Adaptive Filtering. 
New " York, Wiley, 2003. 

3. Simon S. Haykin, Bernard Widrow, Least-mean- 
square adaptive filters, Wiley ;2003. 



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4. S.L. Gay and S. Tavathia, "The Fast Affme Projec- 
tion Algorithm," Proc. 1995 IEEE Int. Conf. 
Acoustics, Speech, and Signal Process, May 
1995, vol. 5, pp. 3023-3026. 

5. K. Ozeki, T. Umeda, "An Adaptive Filtering Algo- 
rithm Using an Orthogonal Projection to an 
Affme Subspace. and Its Properties," Electronics 
and Communications in Japan, Vol.67-A, no. 
5, pp. 19-27, 1984. 

6. Hyun-Chool Shin, Ali H. Sayed, and Woo- Jin 
Song, "Variable Step-Size NLMS and Affme 
Projection Algorithms", IEEE, Signal Processing 
Letters, Vol.1 l,No.2, Feb. 2004. 

7. Seong-Eun Kim, Se-Jin Kong , Woo-Jin Song ,"An 
Affme Projection Algorithm With Evolving Order 
", IEEE Signal Processing Letter, Vol. 16, 
No. 11, Nov 2009. 

8. H.-C. Shin and A. H. Sayed, "Mean- square perfor- 
mance of a family of affme projection algo- 
rithms," IEEE Trans. Signal Processing, vol. 52, 
no.l, pp. 90-102, Jan.2004. 

9. J. Benesty, D.R. Morgan, and J. H. Cho, "A new 
class of doubletalk detectors based on cross- 
correlation," IEEE Trans. Speech Audio Process., 
vol. 8, no. 2, pp. 168-172, Mar. 2000 

10. Thien-An Vu, Heping Ding, and Martin Bouchard 
"A survey of double talk detection scheme 
for echo cancellation applications" 

11. D.L. Duttweiller, "A twelve-channel digital echo 
canceller," IEEE Trans. Comm., vol. 26, pp. 647- 
653, May 1978. 

12. I.Yamada, K.Slavakis, and K.Yamada, "An effi- 
cient robust adaptive filtering algorithm based on 
parallel subgradient projection techniques," IEEE 
Trans. Signal Process., vol. 50, no. 5, pp. 1091— 
1101,May2002 

13. H. Rey, L. Rey Vega, S. Tressens, and J. Benesty, 
"Variable explicit regularization in affine projec- 
tion algorithm: Robustness issues and optimal 
choice," IEEE Trans. Signal Process., vol. 55, no. 
5, pp. 2096-2108, May 2007. 




AUTHORS PROFILE 



Shifali Srivastava received her B. Tech degree 
from H.B.T.I Kanpur, and now perusing 
M.Tech from JUT, Noida. She has done pro- 
jects on all optical networks. Her area of 
interest is signal processing and communica- 
tions. 



M.C. Srivastava received his B.E. degree from 
Roorkee University (now IIT Roorkee), 
M.Tech from Indian Institute of technology 
Mumbai and Ph. D form IIT Delhi in 1974.He 
was associated with I.T. BHU, Birla institute of 
Technology and Science Pilani, Birla institute 
of Technology Ranchi and ECE Dept. JUT 
Sector 62 Noida. He has published around 60 
research papers. His area of research is signal 
processing and communications. He was 
awarded with Maghnad Saha Award for his 
research paper. 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol. 8, No. 3, 2010 



Design and Implementation of Flexible Framework 
for Secure Wireless Sensor Network Applications 



Inakota Trilok 

Department of Computer Science & Engineering 

National Institute of Technology 

Warangal, India 

itrilok @ hotmail . com 



Mahesh U.Patil 
National Ubiquitous Computing Research Centre 
Centre for Development of Advanced Computing 

Hyderabad, India 

maheshp@cdac.in 



Abstract — Secure communications is an interesting and 
challenging research area in Wireless Sensor Networks (WSN) 
fundamentally because of the low power constraints and small 
memory footprints inherent in the technology. In this context, 
there are many hardware platforms like TelosB, MicaZ and 
Mote2 which implement a security layer in hardware, supporting 
multiple modes of operation like encryption, integrity or 
combinations of both. However, not all hardware platforms 
support hardware security which creates avenues of research in 
designing low power security algorithms in software. As with the 
development of security algorithms for WSN applications, there 
is an urgent requirement to create a unified approach for 
application developers by which they can integrate and use 
existing security algorithms thereby maintaining an abstraction 
from the intricacies of the algorithm. 

This paper introduces a flexible framework which 
implements a unified API to add new security algorithms to a 
security library suite. This library integrates existing security 
algorithms like TinySec, MiniSec etc. We also bring out the 
implementation of Advanced Encryption Standard (AES) in 
software supporting its various modes of operation. We have 
integrated this implementation with the unified framework and 
demonstrated its performance and our results. We compare our 
software AES implementation with the Hardware AES 
implementation, in all the supported mode settings. 

Key words- Wireless Sensor Networks; Mote; Link Layer 
Security; Network Layer Security; Hardware Level Security, 
Integrity; Encryption; Authentication. 



I. 



Introduction 



Wireless Sensor Networks (WSN) is a collection of 
distributed autonomous systems called sensor nodes that 
monitor and collect physical data for assessment and 
evaluation. These sensor nodes are very small in size, and are 
limited in resources like CPU, memory and network 
bandwidth. Moreover they are powered through small batteries. 
All these make wireless sensor networks vulnerable to security 
attacks and this is a crucial aspect of the sensor network. Much 
of security is application specific and in applications like 
physical intrusion detection or perimeter protection these are of 
utmost importance. As sensor nodes are powered through 
batteries, security techniques must ideally consume less energy. 
Ironically, some sensor networks need high security which 



leads to higher consumption of energy. In such cases, high- 
energy and rechargeable batteries are used. There are some 
contradictions between the communication energy cost and 
cryptographic cost for WSN (See [1, 2]). So, the security 
technique needed depends on the application that is to be 
deployed into the network. Thus, there needs to be a balance 
between the amount of security that can be provided to these 
networks and resources on the mote. This is different from 
conventional security solutions, since in WSN the security is 
tightly coupled to the application's need. 

TinyOS [22] is a free and open source embedded operating 
system which is specifically designed for wireless sensor 
network application development. It follows a component 
based architecture which enables application developers to 
integrate their application requirements with existing network 
communication protocols. The application and operating 
system is bundled into a final image which is burnt onto the 
hardware thereby creating two tier architecture. Application 
developers should wire a customized network stack for which 
knowledge of low level details of each of the algorithms is 
required. These algorithms could spawn diverse areas like 
network communication, dissemination, time synchronization, 
and security, etc. Moreover, to modify the stack inorder to test 
performance, knowledge of the interfaces provided by alternate 
algorithms also have to be understood. This requirement 
imposes an additional burden on the developer. 

There are number of security algorithms available in 
TinyOS communication protocol stack. Some of them like 
TinySec [3], SenSec [4] and CC2420/CC2430/CC2431 Radio 
AES [5] operate at the link layer, while other algorithms like 
MiniSec [6] exist at the network layer. As is seen above there is 
wide diversity in the implementation and detail of the 
algorithms. This renders migration from one security algorithm 
to another a point of bother for the application developer. A 
uniform access method for all security algorithms is desirable. 
The main contributions of this paper are: 

• Introduction to an adaptive framework for WSN 
applications. 

• A general purpose security library suite composed of 
existing security algorithms for popular sensor node 
hardware platforms. 



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(IJCSIS) International Journal of Computer Science and Information Security, 



• An application developer's perspective in creating a 
custom network stacks specific to his application 
requirements. 

• Support for existing versions of TinyOS providing the 
application developer an abstraction to low level 
implementation of the desired security algorithm 
chosen. The application developer is provided with a 
common API's for setting modes and updating keys for 
all available security algorithms. 

• Implementation and integration of software AES and 
hardware AES with same mode settings like encryption 
only, CBC-MAC integrity-only, counter encryption 
only and counter encryption - CBC-MAC integrity 
with dynamic and static key support. 

The paper is outlined as follows. Section 2 presents the 
background of various security protocols at different levels 
and their limitations, Section 3 formulates the 
implementation of flexible framework and its design goals, 
and introduces software AES and various modes of 
operation used by us and Section 4 describes the results and 
analysis of framework with software AES. Finally Section 
5 concludes with a peek into the future work. 

II. Background 

A. Existing security protocols: 

TinySec [3] which is implemented at Link layer ensures 
low energy consumption but the algorithm is vulnerable to 
replay attacks. MiniSec [6] at Network layer ensures low 
energy consumption but operates only a fixed level of security 
which supports both encryption and authentication. Many 
applications require a combination of both confidential and 
non-confidential data which is not supported by MiniSec. 
However hardware AES security hosts ensures low power 
consumption and combinational levels of security. This feature 
is available for hardware that contains IEEE 802.15.4 
compliant RF transceiver like CC2420/CC2430/CC2431 chips 
[6] and some of the motes that support this radio are TelosB, 
MicaZ and Mote2. Not all motes like IRIS [19] etc support 
hardware security. A software implementation of the AES 
could be a way satisfies these requirements widely. We have 
chosen AES for both efficiency and security reasons (see [8, 
9]). 

B. Supported block ciphers: 

There are many block ciphers available namely Skipjack 
[16], RC5 [15], RC6 [11] and Rijndael [17]. Each cipher is 
chosen based on the need of applications security, memory and 
energy efficiency of the cipher [10]. We have selected Rijndael 
in the configuration of 128/128/10 (keysize/blocksize/rounds) 
but still our library suite supports [192— 256]/128/[ 12 — 14] 
configurations. As, in WSN power is more of concern, 
choosing 128 bit key is more appropriate. 

C. Modes of Block cipher: 

CTR encryption: This is counter mode encryption. To make 
compatible with hardware AES we provided this option [18]. 



Vol. 8, No.3, 
mode provides 



2010 
only 



CBC-MAC Authentication: This 
authentication of the payload [7]. 

CCM Mode: This is Counter mode encryption and CBC-MAC 
authentication mode that features authenticated encryption 
[20]. 

OCB Mode: This Offset CodeBook, is a block-cipher mode of 
operation that features authenticated encryption for arbitrary 
length of data [7]. 

Only-Encryption/Only-Decryption: In this mode, simple 
encryption/decryption operations are performed without any 
mode settings. 

III. The Adaptive Framework For Secure Wsn 
Applications And It's Design Goals 

We propose a flexible framework for programming 
TinyOS. This framework is divided into multiple components 
as shown in Figure 1 and each component contains group of 
protocols: routing protocols, Time- synchronization protocols, 
localization and security protocols. The application developer 
is required to wire a combination of these protocols for specific 
application using the framework. For example, an application 
can use TinyHop routing algorithm, Flooding Time 
Synchronization Protocol (FTSP) [9] with Hardware or 
Software security. In such a case this framework can be 
configured depending on the application's need and the level of 
security required. So, we grouped these protocols into multiple 
components by providing abstraction to the application 
developer for easier access. 





WS N Ap pi ic a t io n 






# 






Common API ' s 






It 






Adaptive Framework f 


[>r WSIM Application 






TinyHop, 
Mult iH op . . . 




FTSP, TPSN. . . 






Routing 
Component 




Time Synchronization 

Component 




TinySec, MiniSec, 

Ha rdwa re AES 

Software AES. . . 




Lo calizati on 
Protocols . . . 






Security Component 


Localization Component 




tt 






Ha rdwa re 





Fig. 1. Adaptive Framework 



Since one of the objectives of this paper is in providing a 
unified security access API to application developers, we 
introduce a use case of the framework with the security 
component described. This security library lists available 
security algorithms, based on the version of TinyOS and the 
hardware platform chosen. There are two actors in the 
framework, the application developer and the system 
component developer. A system component developer 



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integrates security algorithms into the library suite in 
conjunction with the unified access API which we have 
standardized. The system component developer is required to 
create glue logic between the standardized API and the 
algorithm's internal functions. The application developer on the 
other hand is required to use the unified access API to wire his 
application's logic with the required security algorithm. 
Algorithms implementing various modes of block ciphers 
discussed earlier are integrable into the security suite. We have 
integrated our software AES with IEEE 802.15.4 specification 
and supported all modes for existing security algorithms is 
outlined in Table V. We have also integrated security 
algorithms like TinySec [3], MiniSec [6], and Hardware 
Security [5] in the suite. This framework is useful for security 
related experimentations in perspective of both application 
developer as well as System developer. Any new cipher can be 
easily plugged-in and plugged-out. The unified APIs provided 
for existing security algorithms removes the need of changing 
the existing application code. The framework takes care of 
mode settings based on chosen security layer and algorithm. 
This framework is simple in ease of use, flexible and adaptive. 

TABLE I 
Wrapper APIs Provided By Framework 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
algorithm. For example, applications may use TinySec or 
MiniSec, Hardware AES or Software AES etc. 



command error t AFWAupdateKey(uint8_ t * key, set KEY); 
event void AFWAupdateKeyDone(uint8_ t * key); 
command error t AFW As etTransmitMode( uintl6_ t ctrlO, 

uint8_ t ten ); 
command error t AFWAsetReceiveMode ( uintl6_ t ctrlO, 

uint8_ t ten ); 
command error t AFWAsend( uintl6_ t addr, 

message_ t *msg, uint8_ t ten ); 
event void AFWAsendDone( message _ t *msg, 

uint8_ t error); 
command error t AFWAreset(uint8_ t Type); 
command error t AFWAget(uint8_ t *key, get KEY); 



Ease of use: - It provides complete abstraction to the 
application developer and introduces a GUI through which the 
application developer can select components of the lower 
layers. The framework will internally wire and create a 
template for application development. For example, if 
application developer uses IRIS mote and needs software AES 
with various mode settings, a project can be created for IRIS 
mote using WSN IDE [21] and then call commands for mode 
settings. Now the framework makes a setup and loads AES 
library. 

Flexible: - Our framework is flexible, because it has feature 
of plug-in and plug-out facility i.e. any security algorithm can 
easily be integrated. Also, there are several schemes for key 
settings. In such a case dynamic key support is more robust 
than the pre-configured key. We used Java Cryptographic [23] 
functions with mouse movement and keyboard random-key 
generation. In this framework it supports both randomly 
generated dynamic key and static key setting. 

Adaptive: - Level of security is application specific. So, it is 
a choice of application developer to choose type of security 
needed. The framework is adaptive so that it can switch 
between levels of security and provides corresponding security 
layer to the application based on selection of security 



A. Hardware Independent AES with IEEE 802. 15.4 
Specification 

In this section, implementation of the Software AES 
security architecture with IEEE 802.15.4 specification which is 
provided in the framework security component is outlined. 
According to IEEE 802.15.4 specification it has eight different 
security suites [13]. The Table IV, gives five modes and in last 
two modes each have different variants based on the chosen 
MAC value. 




Fig. 2. Configuration file for Software AES 

The above Figure 2 depicts the configuration for software 
AES. We have configured in such a way that n-number of 
modes can be integrate with AES module but only suitable 
mode with AES module is loaded during the compile time. 
This configuration makes developer easier to integrate AES 
module with any new mode. 

We will see each AES security suite in-detail: 

• CTR: This is counter mode encryption. It uses counter 
value and it consists of sender's address and 4-byte 
frame counter. We have not appended any flags and 
block counter to the data payload and hence this 
minimizes the power of mote. The block counter is the 
number of blocks splited into 16 byte blocks within the 
packet. This value can be calculated based on the size 
of the packet that is to be sent. The frame counter is 
maintained by the software AES and it is incremented 
for each packet automatically by software. The send 
API includes frame counter and encrypted payload into 
the data payload of the packet. Below is the code 
snippet for CTR mode encryption. 



uint8_t *payload = call AMSend.getPayload(msg, ten); 

memcpyi smsg.data, payload, ten ); 

call CtrLAESctr encrypt((uint8_ t *)smsg.data, ten, 

sec txl, nonceValue ); 
memcpy{&fcl, &nonceValue[3], 4 ); 
smsg.fc =fcl; 

memcpyipayload, &smsg, fcLen + ten ); 
return call AMSend.sendj addr, msg,fcLen + ten ); 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No.3, 2010 



For example, if h \\ pi,...,p n is frame format then after 
CTR mode encryption, packet sent will be h || z\ ||c,, 
where, c\ =pi ® AES k (Xi), h = header of Tiny Hop, z\ = 
Frame counter, c- x = encrypted pay load, X\ = nonce 
value or counter or initial vector. 

CBC_MACJ|SEC_M [0-7]: This mode provides 
only authentication. And the size of MAC value can 
vary between 4, 6, 8, 10, 12, 14, 16 byte. Here 
CBC_MAC || SEC_M[0- 7] is a macro setting that tells 
library to load AES with CBC_MAC mode. In the 
below code snippet truncateTag() command will 
truncates MAC in the range {4,6,8,10,12,14,16} byte 
based on the SEC_M[0- 7] macro setting i.e. if the 
macro is set to one among the following SEC_M1, 
SEC_M2 ... SEC_M7 then each value maps to the 
range {4,6,8,10,12,14,16} byte while SEC_M0 is 
reserved for future use. By default in CBC-MAC 
mode, length field is authenticated by software. 

In this it supports two protecting modes: 

(a) It protects header of TinyHop routing algorithm as 
well as the data payload. Suppose if h\\p h ...,p n is frame 
format then after CBC_MAC authentication, packet 
that is sent will be 

h\\p h ...,p n || Trunc S Ec _m[o i]{auth{h\p u ...,p n )}. 

(b) Protects only data payload. Suppose if h\\p h ...,p n is 
frame format then after CBCJVIAC authentication, 
packet that is sent will be 

h\\p h ...,p n \\Trunc SE c_M[o i]{auth{p u ...,p n )}. 

Where, h = TinyHop header, p x = plain text. 



call Ccml.AESccm auth((uint8_ t *)smsg.data, len, 

sec txl,KeySizeB ); 
call CcmI.truncateTag((uint8_ t *)smsg.data,len, 

appLen); 
call Ccml.AESccm encrypt((uint8 _ t *)smsg.data, len, 

sec txl,nonceValue ); 
memcpy( &fcl, &nonceValue[3], 4 ); 
smsg.fc = fcl; 

memcpy( payload, &smsg, fcLen + len + appLen ); 
return call AMSend.send( addr, msg,fcLen + len + 
appLen ); 



For example, If h \\p h . . . , p n is frame format then after 
CCM mode, packet sent will be 

h \\zi \\cj,...,c n \\ENC(MAC) 
where, MAC = Trunc SE c u[0-7]{auth(h\pu 
ENC(MAC) = MAC ® AESifa), 
o x = pi ®AES k (xi), Zi = Frame Counter. 



Pn)h 



• AES_ENC: This is simplest mode. It provides simple 
AES encryption operation without any mode settings. 
Below is the code snippet that takes key size and 
pointer to input array as an argument and produces 
encrypted output in the same input array. 



call AesI.startAES((uint8_ t)KeySize, (uint8_ t *)inPut); 



• AES_DEC: This is simplest mode. It provides simple 
AES decryption operation without any mode settings. 
Below is the code snippet that takes key size and 
pointer to input array as an argument and produces 
decrypted output in the same input array. 



call AesI.startAES((uint8_t)Key Size, (uint8_t *)inPut); 



uint8_t *payload = call AMSend.getPayload(msg, len); 

memcpyi smsg.data, payload, len ); 

call CtrLAESctr encrypt((uint8_ t *)smsg.data, len, 

sec txl, nonceValue ); 
memcpy{&fcl, &nonceValue[3], 4 ); 
smsg.fc =fcl; 

memcpyipayload, &smsg, fcLen + len ); 
return call AMSend.send( addr, msg,fcLen + len ); 



• CCM||SEC_M [0- 7]: This is AES counter mode 
encryption and CBC-MAC authentication. Here 
CBC_MAC||SEC_M[0 - 7] is a macro setting that tells 
library to load AES with CCM mode and functionality 
of SEC_M[0- 7] is same as previous mode setting. In 
this mode first it authenticates header of TinyHop 
routing algorithm and data payload using CBC-MAC 
and then encrypts both data payload and MAC using 
AES -CTR mode. Below is the code snippet for CCM 
mode implementation. 



uint8_t *payload = call AMSend.getPayload(msg, 

len); 
memcpy (smsg.data, payload, len ); 
call Ccml.AESccm nonce(nonceValue); 



Figure 3, depicts the complete flow that takes changes in 
the length field of the payload while sending a packet. 



AFWAsend (addr, msg , 


len}; {~ 


Start ) 






\ 




r 


/ Address 


Message, len 




/ 




~f< 




Yes ^s^ If CTR or 




N 


-r 


-^v 


CCM 


"f 


CBC MAC 


1. Append FC 

2. len = fcLen + len 

+ 5ec_m/2+2 




1. Append FC 

2. len = fcLen 

len 


+ 




1. len 


= len + 
sec_m/2+2 


1. len = len 






> 


1 


I 




4r 










* 




c 


St 


3 3 


) 





Fig. 3. AES Security suite 

FC = frame counter, fcLen = frame counter length 
len = length of the payload 

sec_m = Number of bytes in authentication field for CBC- 
MAC, encoded as (M-2)/2. 



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B. Common API 's for Security algorithms 

Since, each security algorithm has its own parameter 
settings; the developer has to know which API is to be used. 
We propose Security interface that contains common wrapper 
API's to set and get parameters for various security algorithms. 
Before choosing security algorithm, application developer will 
choose version of tinyos and for this we created framework 
[21] that lays above wrapper API's. Some of the common 
API's that we provided are: 

a) command error t AFWAupdateKey(uint8_t * key, 

uint8_t setKEY); 

event void AFWAupdateKeyDone(uint8_t * key); 

AFWAupdateKeyO updates old key value to new key value 
where KEY parameter defines which key is to be updated in 
ACL entry. uint8_t *key is a pointer to an array containing 8-bit 
unsigned integers. KEY is 8-bit unsigned integer that takes 
macro value for ACL entries i.e. KEYO or KEY1 or so on. 

b) command error t AFWAsetTransmitMode( uint!6_t ctrlO, 

uint8_t len ); 
command error t AFWAsetReceiveMode ( uintl6_t ctrlO, 

uint8_t len ); 

AFWAsetTransmitModeO / AFWAsetReceiveMode() sets 
transmission and receiver mode for any security algorithm. 
Parameter ctrlO value can be a combination of macros given in 
Table IV and parameter len has value zero if the total payload 
value is to be encrypted/decrypted otherwise len value 
specifies number of bytes to be encrypted or decrypted or 
number of bytes not to be encrypted or decrypted based on the 
context and mode setting of ctrlO. 

c) command error t AFWAsend( uintl6_t addr, message _t 

*msg, uint8_t len ); 
event void AFWAsendDone( message _t *msg, 
uint8_t error ); 

AFWAsend( ) command is similar to AMSendO command. 
This command will set correct length of the transmitted 
message when Hardware/Software AES security is used and 
this command does nothing for other security algorithms. 

d) command error t AFWAreset(uint8_t Type); 

This command is used to reset MAC or Encryption 
initialization vectors of security algorithm. 

e) command error t AFWAget(uint8_t *hey, KEY ); 

This command is used to get key value from ACL entry. KEY 
is 8-bit unsigned integer that takes macro value for ACL 
entries i.e. KEYO or KEY1 or so on and the final result is 
fetched to key. 

IV. Results and Analysis 

We have tested our proposed framework and software 
Advanced Encryption Standard (AES) with various mode 



(IJCSIS) International lournal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
settings using two different motes i.e. IRIS and MicaZ. We 
have taken application that sends encrypted packets using 
TinyHop routing algorithm to the Base Station. And then Base 
Station decrypts the received packets and forwards the packet 
to serial forwarder where the user can view the original packet. 
We sniffed the packets using sniffer to check whether packet is 
encrypted or not in various mode settings. Tables II results are 
obtained after installing software Advanced Encryption 
Algorithm using TinyOS-2.1.0 without TinyHop routing 
algorithm and without our framework into MicaZ and IRIS 
motes. 



TABLE II 
Memory Utilization of Software AES USING TINYOS-2.1.0 

Framework Without Tinyhop Routing Algorithm 



Mote 


ROM 

occupied 

in bytes 

(percentage) 


RAM 

occupied 

in bytes 

(percentage) 


Name of 

Cipher 

and its 

configuration 


MicaZ 


22256(17.38%) 


2196(54.9%) 


AES 128/128/10, 
CCM mode 
with 16-byte 




22258(17.38%) 


2196(54.9%) 


AES 128/128/10 
CBC-MAC with 
16-byte MAC 




22254(17.38%) 


2196(54.9%) 


AES 128/128/10 
CTR mode 




22242(17.37%) 


2196(54.9%) 


AES 128/128/10 
only AES 
Encryption/ 
Decryption 


IRIS 


21308(16.64%) 


2404(30.05%) 


AES 128/128/10 
CCM mode 
with 16- byte 




21306(16.64%) 


2404(30.05%) 


AES 128/128/10 
CBC-MAC with 
16-byte MAC 




21304(16.64%) 


2404(30.05%) 


AES 128/128/10 
CTR mode 




21292(16.63%) 


2404(30.05%) 


AES 128/128/10 
Only AES 
Encryption/ 
decryption 



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Table III results are obtained after installing software 
Advanced Encryption Algorithm using TinyOS-2.1.0 with 
TinyHop routing algorithm and with our framework into 
MicaZ and IRIS mote. The TinyHop routing algorithm 
occupies more memory as to manage routing information. Also 
we have removed distinction between key and frame counter 
[13]. 

TABLE III 

Memory Utilization of Software AES USING TIN YOS -2. 1.0 

Framework WITH TINYHOP Routing Algorithm 



Mote 


ROM 

occupied 

in bytes 

(percentage) 


RAM 

occupied 

in bytes 

(percentage) 


Name of Cipher 

and its 

configuration 


MicaZ 


29848(23.31%) 


3739(93.4%) 


AES 128/128/10 
CCM mode with 
16-byte 




29850(23.32%) 


3663(91.5%) 


AES 128/128/10 
CBC-MAC with 
16-byte 




29774(23.26%) 


3435(85.87%) 


AES 128/128/10, 
CTR mode 




29676(23.18%) 


3359(83.97%) 


AES 128/128/10, 
Only AES 
Encryption/ 
Decryption 


IRIS 


28758(22.46%) 


3923(49.03%) 


AES 128/128/10, 
CCM mode with 
16-byte MAC 




28760(22.46%) 


3831(47.88%) 


AES 128/128/10, 
CBC-MAC with 
16-byte MAC 




28682(22.40%) 


3555(4443%) 


AES 128/128/10, 
CTR mode 




28650(22.38%) 


3359(41.98%) 


AES 128/128/10, 
only AES 
Encryption/ 
Decryption 



V. Conclusion And Future Work 

The framework implemented is unique for WSN 
applications. It has general purpose security library suite 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No.3, 2010 
composed of existing security algorithms for popular sensor 
node hardware platforms. The framework also supports 
existing versions of TinyOS providing the application 
developer an abstraction to low level implementation of the 
desired security algorithm chosen. We also Implemented and 
integrated software AES and hardware AES driver for 
CC2420/CC2430/CC2431 radio chip [5] with same mode 
settings like encryption-only, CBC-MAC integrity-only, 
counter encryption only and counter encryption - CBC-MAC 
integrity with dynamic and static key support. We have tested 
the code using with and without TinyHop routing algorithm. 

The work presented in this paper can be applied to various 
mote platforms. Future extensions are also possible to integrate 
various security modes with simple plug-in and plug-out 
configuration file. Currently the framework is in-built with 
WSN IDE [21] that creates templates for application and then 
application developer has to wire the modules manually. In the 
next design we extend this framework and security modules 
wiring with drag and drop options. 



References 

[I] K. Piotrowski, P. Langendoerfer, S. Peter,"How public key cryptography 
influences wireless sensor node lifetime," Proc. of fourth ACM 
workshop on Security of adhoc and sensor networks^ ASN 06), pages 
169-176,2006. ACM.. 

[2] A. Wander, N. Gura, H. Eberle, V. Gupta, S. C. Shantz,"Energy analysis 
of public-key cryptography for wireless sensor networks," Proc. of Third 
IEEE International Conference on Pervasive Computing and 
Communications (PerCom 05), pages 324-328, March 2005. 

[3] Chris Karlof, Naveen Sastry, David Wagner, "TinySec: Link Layer 

Encryption for Tiny Devices", ACM Conference on Embedded 

Networked Sensor Systems, 2004. 
[4] Tieyan Li, Hongjun Wu, Xinkai Wang, Feng Bao,"SenSec Design, I2R 

Sensor Network Flagship Project"; Technical Report TRvl.0. 
[5] 2.4 GHz IEEE 802.1 5. 4/ZigBee -ready RF Transceiver, Chipcon 

Products from Texas Instruments, http://www.ti.com . 

[6] Mark Luk, GhitaMezzour, Adrian Perringm, Virgil Gligor,"MiniSec: A 
Secure Sensor Network Communication Architeture", ACM 
International Conference on Information Processing in Sensro 
Networks, April 2007. 

[7] Phillip Rogaway, Mihir Bellare, John Black, "OCB: A block-cipher 
mode of operation for efficient authenticated encryption", ACM 
Transactions on Information and System Security (TISSEC), Volume 6, 
Issue 3, pp.365-403, August 2003. 

[8] Devesh Jinwala, Dhiren Patel, K S Dasgupta,"Optimizing the Block 
Cipher Modes of Operations Overhead at the Link Layer Security 
Framework in the Wireless Sensor Networks," Proceedings of the 4 th 
International Conference on Information Systems Security, LNCS, 
pp.258-272, Springer Berlin/Heidelberg, 2008. 

[9] Miklos Maroti, Gyula Simon, Branislav Kusy, and Akos Ledeczi,"The 
flooding time synchronization protocol," in Proceedings of the 2nd 
international conference on Embedded networked sensor systems, 
Baltimore, MD, USA, Nov. 2004, pp. 3949. 

[10] Law, Y.W., Doumen, J., Hartel, P, "Survey and benchmark block 
ciphers for wireless sensor networks"; ACM Transactions on Sensor 
Networks, 2006. 

[II] RC6 cipher - http://people.csail.mit.edu/rivest/Rc6.pdf 

[12] Mingbo Xiao, Xudong Wang, Guangsong Yang,"Cross-Layer Design 
for the Security of Wireless Sensor Networks", Proceedings of the 6th 
World Congress on Intelligent Control and Automation, Jnue 21- 
23,2006 Dalian, China,pp(104-108). 

[13] Naveen Sastry and David Wagner, "Security Considerations for IEEE 
802.15.4 Networks". ACM Workshop on Wireless Security WiSe 2004, 
October 2004. 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No.3, 2010 



[14] Kalpana Sharma, M.K. Ghose, Kuldeep, "Complete Security Framework 
for Wireless Sensor Networks", (IJCSIS) International Journal of 
Computer Science and Information Security, Vol. 3, No. 1, 2009. 

[15] Rivest R; "The RC5 Encryption Algorithm", Proceedings of the Second 
International Workshop on Fast Software Encryption, 1994. 

[16] Skipjack - a representative of a family of encryption algorithms as part 
of the NSA suite of algorithms; 

http://csrc.nist.gov/groups/STM/cavp/documents/skipiack/skipiack.pdf 

[17] J.Daemen, V.Rijmen, "AES Proposal: Rijndael", 

http://www.esat.kuleuven.ac.be/riimen/riindael/riindaeldocV2.zip . 

[18] Helger Lipmaa, Phillip Rogaway and David Wagner. Comments to 
NIST Concerning AES-modes of Operations: CTR-mode Encryption. In 
Symmetric Key Block Cipher Modes of Operation Workshop, 
Baltimore, Maryland, USA, October 20, 2000. 

[19] IRIS motes - http://www.xbow.com/Products/wproductsoverview.aspx . 

[20] Whiting, D., Housley, R. and N. Ferguson, "AES Encryption 

Authentication Using CTR Mode CBC-MAC," IEEE P802.ll doc 

02/00 lr2, May 2002. 

[21] http://www.ubicomp.in/afwa/ 

[22] http://www.tinyos.net/ 

[23] http://iava.sun.eom/i2se/l.4.2/docs/guide/security/CryptoSpec.html 



TABLE IV 

Mode Settings For Various Security Algorithms 



Algorithm 


ctrlO 


len 


AES: 

Stand Alone 


AES STANDALONE ||KEY0 







AES STANDALONE ||KEY1 





AES: 

In-line 


AES INLINE ||[T||R]XKEY[0I1] 
|| CBC_MAC || SEC_M[0 - 7] 


X 




AES INLINE ||[T||R]XKEY[0I1] 
|| CTR 


X 




AES INLINE ||[T||R]XKEY[0I1] 
||CCM 


X 




AES_INLINE ||[[T||R]XKEY[0I1] 
|| CBC MAC II SEC M[0 - 7] 


X 


TinySec 


TINYSEC_AUTH_ONLY 







TINYSEC_ENCRYPT_AND_AUTH 







TINYSEC_DISABLED 







TINYSEC_RECEIVE_ 
AUTHENTICATED 







TINYSEC_RECEIVE_CRC 







TINYSEC_RECEIVE_ANY 





MiniSec 


MINISECU 






TABLE V 

Mode Settings For Various Security Algorithms 



Algorithm 


Macro 


Description 


AES 


AES_ENC 


only AES encryption 




AES_DEC 


only AES decryption 




CTR 


AES Counter-Mode 

Encryption 

This mode is not secure, 

to make compatible with 

hardware 

AES we provided this 

option. 




CBC_MAC 

|| SEC_M[0-7] 


4, 6, 8,10,12,14, 16 byte 
-MAC. AES CBC-MAC, 
it provides only 
Authentication. 




CCM 

|| SEC_M[0-7] 


4,6,8,10,12,14, 
16-byte MAC. AES 
Counter mode 
encryption and CBC- 
MAC authentication 



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Optimizing the Application-Layer DDoS 
Attacks for Networks 



P.Niranjan Reddy 
Head, Dept. of CSE 
KITS, Warangal 
A.P. , INDIA. 
npolala@yahoo.co.in 



K.Praveen Kumar 

Lecturer, Dept, of CSE 

KITS, Warangal 

A.P. , INDIA. 

praveen kumar35@yahoo.co.in 



M .Preethi 

Lecturer, Dept, of CSE 

KITS, Warangal 

A.P. INDIA. 

preethi 0290@yahoo. co. in 



Abstract - The main aim of the proposed 
framework is to implement the Application- 
Layer DDoS Attacks Optimizing for Popular 
Websites that employing legitimate HTTP 
requests to flood out victim resources and to 
implement an effective method to identify 
whether the surge in traffic is caused by App- 
DDoS attackers or by normal Web surfers. 

Keywords: Terms - Application-layer, distributed 
denial of service (DDoS), popular website. 



I. 



INTRODUCTION 



Distributed Denial of Service (DDoS) 
attack is an attempt to make a computer 
resource unavailable to its intended users. This 
attack has caused severe damage to servers and 
will cause even greater intimidation to the 
development of new Internet services. 
Traditionally, DDoS attacks are carried out at 
the network layer, such as ICMP flooding, 
SYN flooding, and UDP flooding, which are 
called Net-DDoS attacks. The intent of these 
attacks is to consume the network bandwidth 
and deny service to legitimate users of the 
victim systems. Among these floodings 
another attack is Botnet[21] which is a network 
of compromised hosts or bots, under the 
control of a human attacker known as the 
botmaster. Botnets are used to perform 
malicious actions, such as launching DDoS 
attacks, sending spam or phishing emails and 
so on. Thus, botnets have emerged as a threat 
to internet community. Peer to Peer (P2P) is a 
relatively new architecture of botnets. These 
botnets are distributed, and small. So, they are 
difficult to locate and destroy. 

Since many studies have noticed this type 
of attacks and have proposed different schemes 
(e.g., network measure or anomaly detection) 
to protect the network and equipment from 
bandwidth attacks, it is not as easy as in the 
past for attackers to launch the DDoS attacks 
based on network layer. To implement DDoS, 



a worm like program is created to simulate 
self-propagation onto many hosts on a 
network. 

When the simple Net-DDoS attacks fail, 
attackers shift their offensive strategies to 
application-layer attacks and establish a more 
sophisticated type of DDoS attacks. 

To overreach detection, the attackers 
attacking the victim web servers by HTTP 
GET requests (e.g., HTTP flooding) and 
pulling large image files from the victim server 
in overwhelming numbers. In another instance, 
attackers run a massive number of queries 
through the victim's search engine or database 
query to bring the server down [4]. Such 
attacks called as application-layer DDoS (App- 
DDoS) attacks. The MyDoom worm [23] and 
the CyberSlam [3] are all instances of this type 
attack. 

On the web, "flash crowd" [6], [7] refers to 
the situation when a very large number of 
users simultaneously access a popular 
website[13], which produces a surge in 
traffic[8] to the Website and might cause the 
site to be virtually unreachable. Because burst 
traffic and high volume are the common 
characteristics of App-DDoS attacks and flash 
crowds, it is not easy for current techniques to 
distinguish them merely by statistical 
characteristics of traffic. 

II. RELATED WORK 

The researchers made an attempt to detect 
DDoS attacks from three different layers: IP 
layer, TCP layer, and application layer. From 
all of these views, researchers are looking into 
various approaches to differentiate normal 
traffic from the attack one. 

Maximum DDoS -related research has 
concentrated on the IP layer. These techniques 
attempt to detect attacks by analyzing specific 
features, e.g., arrival rate or header 
information. For example, Cabrera et al. [9] 
used the management information base (MIB) 
data which include parameters that indicate 



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different packet and routing statistics from 
routers to achieve the early detection. Yuan et 
al [14] used the cross-correlation analysis to 
capture the traffic patterns and then to decide 
where and when a DDoS attack possibly 
arises. Mirkovic et al [15] monitored the 
asymmetry of two-way packet rates and to 
identify attacks in edge routers. Other 
statistical approach for detection of DDoS 
attacks includes IP addresses [16] and time-to- 
live (TTL) values [17]. 

One of the most important research area is 
TCP layer for detecting DDoS attack. For 
example, authors [9] mapped ICMP, UDP, and 
TCP packet statistical abnormalities to specific 
DDoS attacks based on MIB. Wang et al [18] 
used the TCP SYN/FIN packets for detecting 
SYN flooding attacks. In [18], DDoS attacks 
were discovered by examining the TCP packet 
header against the welldefined rules and 
conditions and differentiated the difference 
between normal and abnormal traffic. Noh et 
al [19] attempted to detect attacks by 
computing the ratio of TCP flags (including 
FIN, SYN, RST, PSH, ACK, and URG) to 
TCP packets received at a Web server. 

Ranjan et al [11] used statistical methods 
to detect characteristics of HTTP sessions and 
employed rate-limiting as the primary defense 
mechanism. Yen et al [12] defended the 
application DDoS attacks with constraint 
random request attacks by the statistical 
methods. Other researchers combated the App- 
DDoS attacks by "puzzle," see, e.g., [20]. Jung 
et a/.'s work [7] he used two properties to 
distinguish the DoS and normal flash crowd: 1) 
a DoS event is due to an increase in the request 
rates for a small group of clients while flash 
crowds are due to increase in the number of 
clients and 2) DoS clients originate from new 
client clusters as compared to flash crowd 
clients which originate from clusters that had 
been seen before the flash event. 



-•■■U.'-. • ■!•■:.;- 




Agems 



Rbctlhi. wptait. mfetf 
Attack commands 
Attack traffic 



III. App-DDoS ATTACKS 

In our opinion, the DDoS attack detection 
approaches in different scenario can be 
clustered as: 

• Net-DDoS attacks versus stable 
background traffic. 

• Net-DDoS attacks versus flash crowd. 

• App-DDoS attacks versus stable 
background traffic. 

• App-DDoS attack versus flash crowd. 

The first two scenarios have been well 
studied and can be dealt with by most existing 
DDoS detection schemes while the other two 
groups are quite different from the previous 
ones. 

This is a simple comparison between the 
existing system and proposed system. 



Existing System 


Proposed System 


Consume the network 
bandwidth and deny 


Bandwidth is 
effectively used 


Service to legitimate 
users. 


Service to all users if 
and only if the 
resource is available. 


Abnormalities are 
identified and denied 


Identifying 
abnormalities and 
serve them in 
different priority 
queues. 


Large amount of data 
is required to train. 


Identifies 

abnormalities with 
small amount of 
training data 


Only positive data's 
are used to train 


More accurate 
identification 


Identifying abnormal 
traffic and filter the 
network 


Identifying most 
abnormal traffic and 
filter when the 
network is heavily 
loaded. 



IV. 



DETECTION PRINCIPLE 



Fig 1 . How the attacker can perform attacks on 
App-layer. 



We can cluster the Web surfers and 
evaluate their contributions to the anomalies in 
the aggregate Web traffic. Here the DDoS 
attack is caused only by the authenticated users 
of the Website. Then, different priorities are 
given to the clusters according to their 
abnormalities and serve them in different 
priority queues. The most abnormal traffic 
may be filtered when the network is heavy 
loaded. Here the priority level of the cluster is 
given based on the access time only. The 



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different modules in the implementation are 
given below in the next section. 



V. 

Web Server Module 



MODULES 



• Login 

• Registration 

• Database Design 

• Application Design 
Attacker Module 

• Normal User 

• Abnormal User 
Flash crowd dismisser 

• Data preparation 

• Training 



A. 



Monitoring 

Web Server Module 



Web servers are computers on the internet 
that host website serving pages to viewers 
upon request. This service is referred to as 
web hosting. Every web server has a unique 
address so that other computers connected to 
the internet know where to find it on the vast 
network. When your request reaches its 
destination, the web server that hosts website 
sends the page in HTML code to your 
ipaddress [5]. This return communique travels 
back through the network. Your computer 
receives the code and your browser interprets 
the HTML code then displays the page for you 
in graphic form. 



B. 



Login 



Login module is general for all kinds of Web 
application to authenticate and authorize the 
user's access to the site. To make valid users 
only can access the site, preventing the 
unauthorized access. 



C. 



Registration: 



This module is also common to all the web 
application. Making the users to access the 
site based upon the registration. It may be free 
or cost. In order to authenticate and authorize 
a user, registration is must. 



D. 



Application Design: 



An application which suits for our project 
is designed using the HTML code and the 
relevant technologies. 



E. 



Database Design: 



Once the application has designed then 
Database has to be designed. Here creation of 
the tables related to our project is created. 



Number of tables needed for the application 
has to be decided and the tables are created for 
that. 



F. Attacker Module: 

This module consists of webpage through 
which Attackers attack the victim Web servers 
by HTTP GET requests (e.g., HTTP Flooding) 
and pulling large image files from the victim 
server in overwhelming numbers. In another 
instance, attackers run a massive number of 
queries through the victim's search engine or 
database query to bring the server down. Very 
large number of attackers simultaneously 
accesses a popular Website, which produces a 
surge in traffic to the website and might cause 
the site to be virtually unreachable. 

16000 



12000 



6 8000 



A 

E 

3 

z 



4000 



request num 




2000 4000 6000 8000 10000 
index of time unit (5sec) 

Fig 2. Simple network Attack path 

Normal User: The user login in and acts as 
a normal user there is no abnormality in his 
behaviors. 

Abnormal User: The user login in and acts 
as the abnormal user, the behaviors of the users 
are found to be abnormal (.e.g., attacker who is 
causing the DDoS attack over the target site). 

Flash crowd dismisser: This model is first 
trained by the stable and low- volume web 
workload whose normality can be ensured by 
most existing anomaly detection systems, and 
then it is used to monitor the following web 
workload for a period of 10 min. When the 
period is past, the model will be updated by the 
new collected web workload whose normality 
is ensured by its entropy fitting to the model. 
Then, the model is used in anomaly detection 
for the next cycle. If some abnormalities 
hiding in the incoming web traffic are found, 
the "defense" system will be implemented. 



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VI. 



ARCHITECTURE 



VII. 



APPLICATION 



The process is divided into three phases: 

1 . Data preparation. 

2. Training 



Web servers application DoS attacks allow 
for efficient DoS with only little resources at 
hand, and thus pose a Serious threat to 
organization. 



3. 



Monitoring 



Data preparation: The main purpose of 
data preparation is to compute the AM by the 
logs of the web server. Various user data are 
collected while accessing the sites. 

Training: Train the collected data for the 
abnormalities. Check the user behaviour with 
the predefined threshold. If the user exists the 
threshold are named as the abnormal users 
(.eg., attacker). Likewise all the user data are 
trained and found out the abnormality. 

Monitoring: In the Monitoring phase, 
checks for the resource availability. If the user 
found to be attacker then the resource is 
available means allows that user to access the 
sites (Simply allow the attacker also if and 
only if the resource is available). If the 
resource is not available means, temporarily 
deny that user to access the site. 




Hide speed internet. 

Mobility tracking 
networks. 



in wireless 



Delay 




import java.awt.BorderLayout; 

import java.awt. Color; 

import java.awt. Container; 

import java.awt.Font; 

//import java.awt.Image; 

i//import java.awt. Toolkit; 

import java.awt. event. ActionE vent; 

import java.awt. event. ActionListener; 

import Java. awt. event.KeyEvent; 

rn r i i 




Figure 4. Time delay while transferring 
the file with out attack. 



Figure 3. Proposed Architecture 



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aaaaaaaaaaaaaaaaaa 

Health Information System 

Banking System 

EEIS 

Digital Image Processing 

Chat Express 

Customer Relationship Support 

Network Banking System 

Magazine Management System 

Survey of Organisation(SIIRVEY TOOL S 

ecure Net Auction System 




iii u 








Delay 


2SS60milli sec 











Figure 5. Time delay while transferring the 
file with attack. 



File Edit View Favorites Tools Heip 










Q m • - B I <9r| J 


' Search Vlf Favorite 


# 0* 


§ 


p-uans 


T "II - 


P 









Monitoring Application layer DDOS Attacks 




Enter Your Search swaroop 
CM 



http: ,' /iocaihost ; .< G? t i , ,&»■*:"?<•[ 



Figure 6. Session closing when the 
Attacking is found. 



VIII. CONCLUSION 

Creating defenses for attacks requires 
monitoring dynamic network activities in order 
to obtain timely and signification information. 
While most current effort focuses on detecting 
Net-DDoS attacks with stable background 
traffic, we proposed detection architecture in 
this paper aiming at monitoring web traffic in 
order to reveal dynamic shifts in normal burst 
traffic, which might signal onset of App-DDoS 
attacks during the flash crowd event. Our 
method reveals early attacks merely depending 
on the document popularity obtained from the 
server log. 



REFERENCES 

[1]. IEEE/ACM Transaction on Networking, Vol. 17, 
No. 1, February, 0209. 

[2]. Http://www. linuxsecurity.com/resource_files/intrus 
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[3]. http://en.wikipedia.org/wiki/Denial-of-serviceattac 
k. 

[4]. K. Poulsen, "FBI Busts Alleged DDoS Mafia," 
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[5]. T. Peng and K. R. M. C. Leckie, "Protection from 
distributed denial of service attacks using history- 
based IP filtering," in Proc. IEEE Int. Conf. 
Commun., May 2003, vol. 1, pp. 482-486. 

[6]. I. Ari, B. Hong, E. L. Miller, S. A. Brandt, and D. 
D. E. Long, "Modeling, Analysis and Simulation of 
Flash Crowds on the Internet," Storage Systems 
Research Center Jack Baskin School of 
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Santa Cruz, CA, Tech. Rep. UCSC-CRL-03-15, 
Feb. 28, 2004 [Online]. Available: 
http://ssrc.cse.ucsc.edu/, 95064. 

[7]. J. Jung, B. Krishnamurthy, and M. Rabinovich, 
"Flash crowds and denial of service attacks: 
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[8]. W. Leland, M. Taqqu, W. Willinger, and D. 
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[9]. J. B. D. Cabrera, L. Lewis, X. Qin, W. Lee, R. K. 
Prasanth, B. Ravichandran, and R. K. Mehra, 
"Proactive detection of distributed denial of service 
attacks using MIB traffic variables a feasibility 



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study," in Proc. IEEE/IFIP Int. Symp. Integr. Netw. 
Manag, May 2001, pp. 609-622. 

[10]. S. Noh, C. Lee, K. Choi, and G. Jung, "Detecting 
Distributed Denial of Service (DDoS) attacks 
through inductive learning," Lecture Notes in 
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[11]. S. Ranjan, R. Swaminathan, M. Uysal, and E. 
Knightly, "DDoS-resilient scheduling to counter 
application layer attacks under imperfect 
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rks /papers/ dos-sched.pdf 

[12]. W. Yen and M.-F. Lee, "Defending application 
DDoS with constraint random request attacks," in 
Proc. Asia-Pacific Conf. Commun., Perth, Western 
Australia, Oct. 3-5, 2005, pp. 620-624. 

[13]. C. Roadknight, 1. Marshall, and D. Vearer, "File 
popularity characterisation," ACMSIGMETRICS 
Performance Eval. Rev., vol. 23, no. 4, pp. 45-50, 
Mar. 2000. 

[14]. J. Yuan and K. Mills, "Monitoring the 
macroscopic effect of DDoS flooding attacks," 
IEEE Trans. Dependable and Secure Computing, 
vol.2, no. 4, pp. 324-335, Oct.-Dec. 2005. 

[15]. J. Mirkovic, G. Prier, and P. Reiher, "Attacking 
DDoS at the source," in Proc. Int. Conf. Network 
Protocols, 2002, pp. 312-321. 

[16]. T. Peng and K. R. M. C. Leckie, "Protection from 
distributed denial of service attacks using history- 
based IP filtering," in Proc. IEEE Int.Conf 
Commun., May 2003, vol. 1, pp. 482-486. 

[17]. B. Xiao, W. Chen, Y. He, and E. H.-M. Sha, "An 
active detecting method against SYN flooding 
attack," in Proc. 11th Int. Conf. Parallel Distrib. 
Syst., Jul. 20-22, 2005, vol. 1, pp. 709-715. 

[18]. H.Wang, D. Zhang, and K. G. Shin, "Detecting 
SYN flooding attacks," in Proc. IEEE INFOCOM, 
2002, vol. 3, pp. 1530-1539. 

[19]. S. Noh, C. Lee, K. Choi, and G. Jung, "Detecting 
Distributed Denial of Service (DDoS) attacks 
through inductive learning," Lecture Notes in 
Computer Science, vol. 2690, pp. 286-295, 2003. 

[20]. S. Kandula, D. Katabi, M. Jacob, and A. W. 
Berger, "Botz-4-Sale: Surviving Organized DDoS 
Attacks that Mimic Flash Crowds,"MIT, Tech. Rep. 
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[21]. James Binkley and Suresh Singh. An algorithm for 
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[23]. "Incident Note IN-2004-01 W32/Novarg. A 
Virus," CERT, 2004. [Online]. Available: 
http://www.cert.org/incident_notes/ IN-2004- 

01. html 

AUTHORS PROFILE 



P.NIRANJAN REDDY received 
the B.E Computer Science from 
Nagpur University in 1992 and 
M.Tech (Computer Science and 
Engineering) from NIT, 

Warangal in the year 2001. He 
worked as a Lecturer and 
Assistant Professor in the 
department of CSE of KITS, 
Warangal, Since 1996. He is 
doing a part-time research in 
Kakatiya University, Warangal 
since 2007. He authored two text 
books, Theory of computation 
and Computer Graphics in the 
field of Computer Science. He 
published 3 papers inlnternational 
Journals and 6 papers in 
International Conferences. 



K.PRAVEEN KUMAR has 

been working as a lecturer in 
Dept. of CSE, KITS,Warangal 
in Andhra Pradesh,INDIA for 
the last 2 years. He has 
completed his B.tech and 
M.tech from KITS warangal. 
He has published a research 
paper at a National level 
Conference 



M.PREETHI has been 
working as a lecturer in 
Dept of CSE in KITS, 
Warangal in Andhra 
Pradesh, INDIA for the last 
3years. She took her 
M.Tech degree from 
KITS,Warangal. 





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Survey - New Routing Technique for Grid Computing 



R.RAMESHKUMAR, 

Research Schlolar, 

J.N.T.University, 

Kukatpally, 

Hyderabad. 

rrameshl968@gmail.com 



Dr. A.DAMODARAM 
Director/ U.G.C Academic Staff College,. 
J.N.T.University, 
Kukatpally, 
Hyderabad. 
adamodaram(%jntuap.ac.in 



Abstract- Trust plays an indispensable role in grid 
computing. Trust-management systems provide applications 
with a standard interface for getting answers to such 
questions and provide users with a standard language for 
writing the policies and credentials that control what is 
allowed and what isn't. Using a trust-management system 
for controlling security-critical services frees the application 
developer from a number of often difficult design and 
implementation issues and allows users to take advantage of 
a flexible, standard, application-independent language for 
specifying policy. In this paper, we develop trust 
management architecture for trust enhanced Grid security 
incorporating a novel trust model which is capable of 
capturing various types of trust relationships that exist in a 
Grid system and providing mechanisms for trust evaluation, 
recommendations and update for trust decisions. The 
outcomes of the trust decisions can then be employed by the 
Grid security system to formulate trust enhanced security 
solutions. Here we put forth ant algorithm for 
implementation. The ant colony algorithm is an algorithm 
for finding optimal paths that is based on the behavior of 
ants searching for food. At first, the ants wander randomly. 
When an ant finds a source of food, it walks back to the 
colony leaving "markers" (pheromones) that show the path 
has food. When other ants come across the markers, they are 
likely to follow the path with a certain probability. If they 
do, they then populate the path with their own markers as 
they bring the food back. As more ants find the path, it 



gets stronger until there are a couple streams of ants 
traveling to various food sources near the colony. 

Key words - Grid computing, security, trust, Ant Colony, 
Service Request. 

I. INTRODUCTION 

Routers use routing algorithms to find the best 
route to a destination. When we say "best route," we 
consider parameters like the number of hops (the trip a 
packet takes from one router or intermediate point to 
another in the network), time delay and communication cost 
of packet transmission. Based on how routers gather 
information about the structure of a network and their 
analysis of information to specify the best route, we have 
two major routing algorithms: global routing algorithms and 
decentralized routing algorithms. In decentralized routing 
algorithms, each router has information about the routers it 
is directly connected to ~ it doesn't know about every router 
in the network. These algorithms are also known as DV 
(distance vector) algorithms. In global routing algorithms, 
every router has complete information about all other 
routers in the network and the traffic status of the network. 
These algorithms are also known as LS (link state) 
algorithms. 
• Routing Components 

Routing involves two basic activities: determining 
the optimal routing paths for destination networks and 



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transporting information groups, also known as packets, 
through an internetwork. Within the context of routing, the 
latter can be referred to as switching. 

• Path Determination 

A metric is a standard of measurement, such as 
path length, that is used by routing algorithms to determine 
the optimal path to a destination. To aid in this process of 
path determination, routing algorithms initialize and 
maintain routing tables, which contain route information. 
This information can vary widely depending on which 
routing algorithm generated the routes. Routing algorithms 
fill routing tables with a list of networks and its 
corresponding "next hop" on the way its destination. When 
a router receives an incoming packet, it checks the 
destination address and attempts to associate this address 
with a next hop. 
Algorithm Types 

• Static versus dynamic 

• Single-path versus multi-path 

• Link state versus distance vector 

• Dynamic vs. Static 

Static routing algorithms are hardly algorithms at 
all, but are table mappings established by the network 
administrator prior to the beginning of routing. These 
mappings do not change unless the network administrator 
alters them. Algorithms that use static routes are simple to 
design and work well in environments where network traffic 
is relatively predictable and where network design is 
relatively simple. 

Because static routing systems cannot react to 
network changes, they generally are considered unsuitable 
for today's large, changing networks. Most of the dominant 
routing algorithms in the 1990s are dynamic routing 
algorithms, which adjust to changing network circumstances 
by analyzing incoming routing update messages. If the 
message indicates that a network change has occurred, the 



routing software recalculates routes and sends out new 
routing update messages. These messages permeate the 
network, stimulating routers to rerun their algorithms and 
change their routing tables accordingly. 
Dynamic routing algorithms can be supplemented with 
static routes where appropriate. A router of last resort (a 
router to which all unroutable packets are sent), for 
example, can be designated to act as a repository for all 
unroutable packets, ensuring that all messages are at least 
handled in some way. 

• Single-Path vs. Multipath 

Some sophisticated routing protocols support 
multiple paths to the same destination. Unlike single-path 
algorithms, these multipath algorithms permit traffic 
multiplexing over multiple lines. The advantages of 
multipath algorithms are obvious: They can provide 
substantially better throughput and reliability. 

• Link State vs. Distance Vector 

Link-state algorithms (also known as shortest path 
first algorithms) flood routing information to all nodes in 
the internetwork. Each router, however, sends only the 
portion of the routing table that describes the state of its own 
links. Distance- vector algorithms (also known as Bellman- 
Ford algorithms) call for each router to send all or some 
portion of its routing table, but only to its neighbors. In 
essence, link- state algorithms send small updates 
everywhere, while distance- vector algorithms send larger 
updates only to neighboring routers. 

Because they converge more quickly, link- state 
algorithms are somewhat less prone to routing loops than 
distance- vector algorithms. On the other hand, link- state 
algorithms require more CPU power and memory than 
distance- vector algorithms. Link-state algorithms, 
therefore, can be more expensive to implement and support. 
Despite their differences, both algorithm types perform well 
in most circumstances. 



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Trust Management Architecture 

Using a trust-management system for controlling 
security-critical services frees the application developer 
from a number of often difficult (and subtle) design and 
implementation issues and allows users to take advantage of 
a flexible, standard, application-independent language for 
specifying policy. Before trust management, every 
application had to provide its own mechanisms for 
specifying policy, interpreting credentials, and binding user 
authentication with the authorization to perform 
"dangerous" operations. Trust-management systems, on the 
other hand, provide a simple interface that takes care of all 
of these things. All the application designer has to do is 
identify the trust management questions in the application 
and formulate appropriate queries to the trust-management 
system. 

Remote (Untr listed) 





untmsiad ' 
client !_*• 








client/ 
peed 

JiC'j 

etc 








Application 








KeylMcte 
API 


V 




app:ica1i"n 
policy 




s'lerr.' 
peer* 
user/ 
etc 










Local 

KeylMcte 

Interpreter 

trust 
system 












Appi 


cation 


Keynote ^ 
quaries 






KeyNote 
API 








cJwnl/ 

peer/ 
user/ 

etc 






app i canon 
policy 










Application 










dienf 
peer/ 
user/ 

etc 










KeylVoEe 
API 






\ 






appTicalion 
policy 




^ 




Credential 
management 
fPKI) j 








client/ 
peer/ 
user/ 
etc 


___tnid 

















Fig. 1 . Keynote trust Management Architecture 

II. Impact of ant algorithm on grid computing 

Grid computing is a term used to describe both a 
platform and type of application. A Grid computing platform 
dynamically provisions, configures, reconfigures, and de 
provisions servers as needed. Servers in the Grid can be 
physical machines or virtual machines. The grid computing 
environment typically include other computing resources 
such as storage area networks (SANs), network equipment, 
firewall and other security devices. Grid computing also 
describes applications that are extended to be accessible 



through the Internet. These Grid applications use large data 
centers and powerful servers that host Web applications and 
Web services. Anyone with a suitable Internet connection and 
a standard browser can access a cloud application. The 
primary components of Grid architecture are: 

• Users/Brokers: Users or brokers acting on their 
behalf submit service requests from anywhere in 
the world to the Data Center and Grid Servers to be 
processed. 

• SLA (Service Level Agreements) Resource 
Allocator: The SLA Resource Allocator acts as the 
interface between the Data Center/Grid service 
provider and external users/brokers. It requires the 
interaction of the defined scheduled mechanisms to 
support SLA-oriented resource management. 

To access the grid resources and execution, it can 
be divided in to three phases like resource recovery, 
scheduling, and executing. In the second phase find the best 
match between the set of jobs and available resources. The 
second phase is a NP-hard Problem [6]. The computational 
grid is a dynamic and unpredictable behavior. They are: 

Computational performance of each resource varies 
from time to time. 

• The connection between computers and mobile 
phones may be unreliable. 

• The resources may join or give up the grid at any 
time 

• The resource may be occupied without a 
notification. 

The scheduling of grid architecture is dynamic in 
nature and moreover Grid middleware and applications are 
using local scheduling and data co-scheduling. The approach 
of replication has been also applied and assisted in 
scheduling and optimization of replication. There are 
different existing algorithms like the Genetic algorithm (GA) 
is used for searching large solution space. On other hand, 



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simulated Annealing (SA) is an iterative technique that 
considers only one possible solution for each meta-task at a 
time. 

ACO algorithm can be interpreted as parallel 
replicated Monte Carlo (MC) systems. MC systems are 
general stochastic simulation systems, that is, techniques 
performing repeated sampling experiments on the model of 
the system under consideration by making use of a stochastic 
component in the state sampling and/or transition rules. 
Experimental results are used to update some statistical 
knowledge about the problem. In turn, this knowledge can 
also be iteratively used to reduce the difference in the 
estimation of the described variables and directing the 
simulation process toward the most interesting state space 
regions. Analogously, in ACO algorithms the ants sample the 
problem's solution space by repeatedly applying a 
stochastic decision policy until a feasible solution of the 
considered problem is found. The sampling is realized 
concurrently by a collection of different instantiated replicas 
of the same ant type. Each ant "experiment" allows to 
adaptively modifying the local statistical knowledge on the 
problem structure. The algorithm is recursive in nature. 

III. PROPOSED Algorithm 

The classic ant colony algorithm can be described as 
follows: 



Step 1 . Initialize 
Step 2. Loop 



/* An iteration */ 



Step 3. Each ant is positioned on a starting node. 
Loop /* A step */ 

Step 4. Each ant applies a state transition 
rule to incrementally build a solution and a local 
pheromone updating rule until all ants have built a 
complete solution 

Step 5. Global pheromone updating rule is applied until 
end condition. 



Step 6. Stop further iterations 

Each edge between node (r, s) has a distance or cost 
associate 8 (r, s) and a pheromone concentration T (r, s). 
The equation 1 is the state transition rule, which is a 
probabilistic function for each node u, which has not been 
visited by each placed ant on node r. 



***•'>•■ E[T(r,u)][f,{r,u)p 



(1) 



The parameter P determine the relevance of the 
pheromone concentration compared with the distance or 
cost, T (r, s) Global pheromone updating rule can be applied 
as: 



7-(r, s) — (1 - a)r(r. s) + SArf e (t\ s j 



(2) 



Where a is the pheromone evaporation factor 
between and 1 and At, (r, s) is the reverse of the distance or 
cost done by ant k, if (r, s) is its path and is if it is not in the 
path. The steps can be modified to manage grid architecture. 
The grid is visualized is the collection of clustered services, 
hence the live services of grid behaves like an ant, when it 
find its file object, the ant died. Subsequently, considering the 
prime component of grid computing, the compute grid and 
storage grid can be modeled as virtual services of grid. 
Every time a request is processed on a grid cluster site, T is 
updated for all the site connections and thus the "(2)" can be 
modified by associating a parameter t . 



t(r,s) = (1-oc )T(r,s) + 2AT, (r,s) 



(3) 



The dot operator represents time for each grid scheduling 
service. Therefore, the a is introduced which expresses 
the evaporation factor under time slot of grid service. The 
heuristic can be divided into two categories for grid-based 
services e.g. on-line mode service and the batch mode service. 
In online mode, whenever a request arrive, it immediately 



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allocate to the first free resource allocator. The arrival order of 
the request in grid is important in this method. Here, each 
service request is considered only once for matching and 
scheduling. In batch mode, the requests are collected; the 
scheduler considers the approximate execution time for each 
task and use heuristic approach to possibly make better 
decision. The function free [j] - return time, when the 
resource allocators Mj will be free. We consider, 

free[j] = I A +ETij 5 
where, I A is the initial time slot of request of service made on 
the grid architecture and ETy is the execution time matrix of 
request ^ on resource allocator m. 

The scheduling of resource allocator on the grid 
service proposes the probability of servicing the request: 



phij Tlij ( 1/ETij ) 



Pij = 



I phij tin ( 1/ETij ) 

Where, - n y is the attractiveness of the move as 
computed by heuristic information indicating a prior 
desirability of that move, phy fast and accuracy of the grid 
service in the past (with lower a) to make that particular 
move (it represents therefore a posterior service 
accomplishment indication of the desirability of that request) 
ETy Execution Matrix of service and resource allocator. In 
this proposed model, we select the highest probability's T 
and 'j' are the next request of service ri executed on the 
resource allocator j. 

IILCONCLUSION 

This paper is the first to develop trust management archi- 
tecture for Grid security solutions based on Subjective 
Logic. We have identified the requirements of trust 
management for Grid computing from security point of 
view. We then develop trust management architecture to 
meet the requirements defined. This trust management 
architecture is designed to be transparent to the Grid 



platforms. It thus can easily be instantiated in a practical 
application as a separate layer, and thus allows seamless 
integration to different Grid computing platforms. Once 
instantiated this architecture allows explicit trust policies to 
be defined and managed. In this paper, a heuristic algorithm 
based on modified ant colony optimization has been proposed 
to initiate the service load distribution under grid computing 
architecture. The simulation doesn't consider the fault 
tolerance issues. Due to absence of any restore time in service 
and resource allocator distribution, it is expected that 
continuous ant colony with other modified parameters could 
demonstrate better results compared to other optimization 
models, even in faulty service request and disrupted resource 
allocator. 

REFERENCES 

[1] F. A. Maheswaran and M., "Evolving and managing 
trust in grid computing systems," in Proceedings of the 
2002 IEEE Canadian Conference on Electrical 
Computer Engineering, 2002. 

[2] L. Shen and J., "A mission-aware behavior trust model 
for grid computing systems," in Intl Workshop on Grid 
and Cooperative Computing (GCC2002), Sanya, China, 
Dec. 26, 2002. 

[3] S. Song and K. Hwang, "Fuzzy trust integration for 
security enforcement in grid computing," in 
International Symposium on Network and Parallel 
Computing(NPC2004), submitted March 22, 2004. 

[4] C. Lin and V. Varadharajan, "Trust relationship in 
mobile agents - a reflexive approach," in Proceedings of 
Internation Conference of Agent-Based Technologies 
and Systems 2003 (ATS03), Calgary, Canada, August 
2003, pp. 81-88. 

[5] A. Abdul-Rahman and S. Hailes, "Using 
recommendations for managing trust in distributed 
systems," In Proceedings of IEEE Malaysia Inter- 
national Conference on Communication 7 97 (MICC'97), 
Kuala Lumpur, Malaysia, 1997. 

[6] M. R. Thompson, D. Olson, R. Cowles, S. Mullen, and 
M. Helm, "Ca-based trust model for grid authentication 



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and identity delegation," in 

http://www. gridcp. es. net/Documents/GGF6/TrustModel 
-finalpdf . Grid Certificate Policy WG, October 2002. 

[7] J. Broch, D. Maltz, D. Johnson, Y. Hu and J. Jetcheva, 
"A Performance Comparison of Multi Hop Wireless 
AdhocNetwork Routing Protocols", Carnegie Mellon 
MONARCH Project, October 1998, 

http://www.monarchxsxmnu.edu/ . 

[8] C.E.Perkins and E.M.Royer, "Ad-Hoc On Demand 
Distance Vector Routing", Proceedings of the IEEE 
Workshop on Mobile Computing Systems and 
Applications WMCSA), February 1999. 



Prof. R.Rameshkumar is pursuing his 
PhD at JNT University, Hyderabad 
under the guidance of 

Prof.Dr.A.Damodaram, Director of 
UGC Academic Staff College of JNT 
University Hyderabad. He has 
obtained his Bachelor Degree in 
Computer Science and Engineering 
from Mookamibigai College of 
Engineering (Bharathidasan 

University) and Master Degree in 
Computer Science and Engineering from Arulmigu 
Kalasalingam College of Engineering(M.K.University). 




[9] C.E.Perkins and E.M.Royer, "Ad-Hoc On Demand 
Distance Vector Routing", Proceedings of the IEEE 
Workshop on Mobile Computing Systems and 
Applications (WMCSA), February 1999. 

[10] E. Bonabeau, M. Dorigo and G. Theraulaz, Swarm 
Intelligence: From Natural to Artificial Systems, 
OxfordUniversity Press, 1999. 

[11] M. Dorigo and G. DiCaro, "Ant Colony Optimization: 
a New Meta-Heuristic", Proc. 1999 Congress on 
Evolutionary Computation, July 6-9, 1999, pp. 1470- 

1477. 



J He joined as Faculty of Computer 

K Science and Engineering in 1989 at 

JNTU, Hyderabad. He worked in the 
JNTU in various capacities since 
1989. Presently he is a professor in 
Computer Science and Engineering 
^^^ Department. In his 19 years of 
service Dr. A. Damodaram assumed 
office as Head of the Department, 
Vice-Principal and presently is the 
Director of UGC Academic Staff 
College of JNT University Hyderabad. He was board of 
studies chairman for JNTU Computer Science and 
Engineering Branch (JNTUCEH) for a period of 2 years. He 
is a life member in various Professional bodies. 
He is a member in various academic councils in various 
Universities. He is also a UGC Nominated member in 
various expert/advisory committees of Universities in India. 
He was a member of NBA (AICTE) sectoral committee and 
also a member in various committees in State and Central 
Government 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 



A Forager Bees Behaviour Inspired approach to 
predict the forthcoming navigation pattern of 

online users 



V.Mohanraj 

Assistant Professor/IT 

Sona College of Technology 

Salem, Tamilnadu, INDIA 

b vmohanraj @yahoo . in 

J Senthilkumar 

Assistant Professor/IT 

Sona College of Technology 

Salem, Tamilnadu, INDIA 

rajkrishcounty@gmail.com 



Dr.R.Lakshmipathi 

Professor/EEE 

St.Peters Engineering College (Deemed University) 

Chennai, Tamilnadu, INDIA 

drrlakshmipathi@yahoo.com 

Y.Suresh 

Assistant Professor/IT 

Sona College of Technology 

Salem, Tamilnadu, INDIA 

shuresh_22@yahoo.co.in 



Abstract — The World Wide Web is continuously growing and 
become de facto place to conduct online business. In the current 
internet world, peoples are more attracted towards participating 
in the e-commerce sites. The real challenge for the web master of 
any such website is to find the users need in advance and provide 
the resources pages that keep them interested in browsing their 
site. It is easy for any unsatisfied user to reach out the 
counterpart site in a single click. Many Web usage mining 
methods were adopted to work on web server log and predict the 
forthcoming navigation pattern of user. However, the accuracy of 
the methods can't satisfy the user especially in huge site. 

This paper presents the forager bees behaviour inspired Forager 
agent based architecture that uses its collective intelligence for 
predicting the forthcoming navigation pattern of user. Our 
practical implementation shows that accuracy and coverage 
measures are very much improved than existing methods. 

Keywords-Web Usage Mining; Web Personalization; Artificial 
Bee Colony. 



I. 



Introduction 



In the current internet world, there are many e-commerce 
sites compete with each other to attract the user. It becomes 
mandatory for web master to predict the future navigation of 
user and recommend those to users. This makes the user to 
browse the site with lot of satisfaction. In case of unhappiness, 
it's easy for online user to switch over to another e-commerce 
site that provides the same kind of service. 

All the e-commerce sites are focusing on how to provide 
the excellent personalized access to users on their sites. The 
solution of the problem is web usage mining (WUM). WUM 
[8] is part of web mining which deals with the extraction of 
knowledge from server log file [4] [5]. Source data mainly 
consists of the logs, that are collected when user access web 
server and might be represented in standard format. WUM has 
become very critical for website management, creating 



adaptive website, business and support services, 
personalization and network traffic flow analysis. 

Typically, the WUM based forthcoming navigation 
pattern capturing process can be divided into FrontEnd and 
BackEnd with respect to the web server activity [6]. The 
activities of BackEnd component is focused on building the 
knowledge base by analyzing server log file which records 
user web usage data. The activities of the FrontEnd component 
are classifying the current user navigations to any one of 
cluster formed in BackEnd Phase and infer the useful pattern 
to predict the future navigations of user. A particular feature of 
our paper is that achieving the accuracy of excellence in 
predicting the forthcoming navigation pattern of user using the 
collective intelligence of Artificial Bee System [15]. This 
system is relatively new member of swarm intelligence. It tries 
to model natural behaviour of real honey bee in food foraging. 
Honey bee use several mechanisms like waggle dance, round 
dance and tremble dance to exchange the information about 
location and profitability of food source. This makes them a 
good candidate for developing new intelligent search 
algorithms. Artificial Bee system has three area of study: 
Foraging Behaviour, Marriage Behaviour and Queen Bee 
concept. Our paper focuses on the usage of foraging behaviour 
of bee in the FrontEnd Phase of Forager architecture to 
achieve excellence in capturing the forthcoming browsing 
pattern of online users. 

The paper is organized as follows. In section II, we review 
the related automatic recommendation systems and reported as 
literature survey. In section III describes the different phases 
of Forager agent based architecture and focuses on the 
Greatest Common Subsequence detection algorithm which is 
used by foragers and Intuition Deductive inference engine 
used by the onlooker in the FrontEnd phase of the architecture. 
In section IV, the illustration of Forager Agent system is 
explained. In section V, the results of our practical 
implementation are reported. Finally, section VI concludes 
our work. 



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II. Related Works 



Recently, a number of studies have been proposed to 
capture the forthcoming navigation pattern of web users. We 
have conducted investigation on different WUM system [10] 
and architecture that can be matched with our proposed 
system. 

Analog [1] is one of the first web usage mining systems. It 
consist of online and offline component. The offline 
component builds session clusters by analyzing user 
navigation pattern recorded in the log file. In the online 
component part, active user session is classified according to 
the generated model. The classification allows identifying the 
pages matches with the active session and returning the 
requested page with list of suggestion. Clustering approach of 
system affected by several limitation especially scalability and 
accuracy. There is variety of clustering algorithms available 
for usage. Each approach could have different type of cluster 
[Exclusive (K-Means), Overlapping (Fuzzy C Means) and 
Hierarchical]. It's difficult to compare the performance of 
algorithm on large dataset like web log. In addition, Clustering 
approach used in all recommender system needs to be back up 
by excellent classification method. Analog did not have the 
proper classification approach over the overlapping cluster. 

A Web personalizer system [11] provides dynamic 
recommendation, as a list of hyperlinks to users. In the Web 
personalizer system, analysis is based on the usage data 
combined with structure formed by the hyperlinks of site. 
Aggregated usage profile is obtained by applying data mining 
technology [i.e clustering, association rule] in pre-processing 
phase [12]. In this phase web server logs are converted into 
cluster made up of set of pages with the common usage 
characteristic. The online phase considers the active user 
session in order to find match among user activities and 
discover usage profile. Matching entries are then used to 
compute a set of recommendations which will be inserted into 
the last requested page as a list of hypertext links. 
Webpersonalizer is good example two tier architecture for 
personalized system. However the accuracy of the 
Webpersonalizer is affected by association rule mining [16] 
used for discovery of frequent item set in web log data. The 
main problem with the association rule mining method is 
discovery of contradictory association rules. As a result of 
inconsistent rules, predicting accuracy of system is degraded. 
Even the non redundant association rule mining algorithm 
does not help the system because of the web log data nature 
where the number of page hits is high. 

Another WUM system called SUGGEST [2] provides 
useful information to optimize web server performance and 
make easier the web user navigation .SUGGEST adopts a two 
level architecture composed by an offline creation of historical 
knowledge and online component that understands the users 
behaviour .SUGGEST uses the markov model for calculating 
the probability of a page the web user visit in future after 
visiting pages in the same session. This system uses the high 
order markov model [7] to improve the accuracy. However, 
the system can't be used for web site made up of large number 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
of pages due to high space complexity. The limitation of 
system might be a) the memory required to store web server 
pages is quadratic in the number of pages. It is a sever 
limitation in the huge web site, b) SUGGEST does not permit 
us to manage web site made up of pages dynamically 
generated. 



Our survey reveals that there is a race for finding 
architecture [3] [13] and classification algorithm to improve 
the accuracy of capturing forthcoming navigation pattern of 
online users. But still the accuracy does not meet the 
satisfaction. In our work, we propose advanced Forager agent 
based architecture and novel user navigation classification 
approach in the architecture for improving accuracy and space 
complexity. Our Forager agent based architecture is the 
inspiration from artificial bee colony introduced by [14] [15]. 

1 . Each employed bee determines a food source, which 
is also representative of a site, within the neighbourhood of the 
food source in its memory and evaluates its profitability. 

2. Each employed bee shares its food source 
information with onlookers waiting in the hive and then each 
onlooker selects a food source site depending on the 
information taken from employed bees. Each onlooker 
determines a food source within the selected site by herself 
and evaluates its profitability using the collective intelligence. 

3. Employed bees whose sources have been abandoned 
become scout and start to search a new food source randomly 
(Fluctuation). 

The step (2) of the algorithm is implemented in the 
FrontEnd phase of Forager agent architecture. In the FrontEnd 
Phase, fleet of forager is originated by onlooker agent on 
number of clusters formed in the BackEnd Phase. Each forager 
search the web pages in the cluster based on reinforcement 
given from onlooker agent. Each forager executes the Greatest 
Common Subsequence detection algorithm on its cluster of 
web pages and also runs the scoring algorithm. The final score 
of each forager is received by onlooker agent. In case of more 
than one profitable cluster, it is the onlooker agent that runs 
the intuition deductive inference engine to choose the best one 
among the alternatives. Finally, recommends the selected one 
as forthcoming browsing pattern of the user. 

III. Different Components Of Two Tier Forager 
Agent Based Architechutre 

According to different functionality, our proposed 
architecture can be divided into two main phase Back End and 
Front End. Both these phases are tightly coupled with each 
other and work closely together. The Figure 1 and 2 depicts 
the BackEnd and FrontEnd architecture of two tier Intelligent 
Forager Agent respectively. In the Back End Phase there are 
two main module, Data pre-processing and user navigation 
mining. The main modules of the Front End phase are 
onlooker agent, forager agent and Intuition deductive 
inference engine. 

A. BackEnd Phase of Architecture 

Two main major modules of the BackEnd Phase are 
Data pre-processing and user navigation pattern mining. In 



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this phase, we perform Data pre-processing on server log to 
capture navigation session and after that we apply algorithm to 
mine user navigation pattern. The Detailed module of 
BackEnd Phase is shown in Figure 3. 




(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
about actual page. We employ the content based information to 
enrich web log data in the form of maintaining ontology for each web 
page in the Array data structure where each index corresponds to 
page number of web site. This information will be used by the 
Intuition deductive engine of Front End for the choosing the best 
classification. 



Log Files 



Data Pre Processing 



User Navigation Mining 



Navigation Profile O/P 



Figure 1 . BackEnd Phase of Two Tier Forager Agent Architecture 



Navigation Profile 
Output 




Forager Agents 



Intuition Deductive 
Inference Engine 



Captured forthcoming 

Browsing pattern of 

user 




Knowledge 
Base 



QD 



Log Files 




Data Pre-processing 



Content & structure 
retrieving 



Session Identification 



Data Cleaning 



Dntn collection 



ser Navigation Mining 



Navigation Pattern Modelling 



Clustering 

¥ 



Navigation pattern 
Profile 



Figure 3. Components of BackEnd 

b) User Navigation Mining 

After the data pre-processing, we perform a navigation 
pattern mining on the identified session. We perform 
clustering which aims to group session into clusters based on 
their sharable properties. These patterns will be further used to 
facilitate the user profiling process of the system. It includes 
two main modules: 

1) Navigation Pattern Modeling 



Figure 2. FrontEnd Phase of Two Tier Forager Agent Architecture 

a) Data Pre-processing 

The pre-processing of web logs is usually complex and time 
demanding. It comprises of four different tasks 1) Data collection: A 
flat file was constructed from original weblog file. Each record of the 
file consists of time, ip address, name, requested resource (URL) and 
HTTP Status code. 2) Data Cleaning: In this step, we perform the 
removal of all the data tracked in web log that are useless for mining 
purpose such as Navigation session performed by robots and web 
spider. 3) Session Identification and reconstruction: it involves i) 
Identifying the different users session from usually very poor 
information available in log files and ii) Reconstructing the user's 
navigation path within the identified session. 4) Content and 
Structure Retrieving: Mostly all WUM uses the visited URL as the 
poor source of information. They do not convey any information 



In this step, web pages accessed are modeled as undirected 
Graph G= (V, E). The set Vertex (V) identifies the different 
web pages hosted on the web server model. The edge weights 
are determined by the following equation 



WPij = 



C\j_ 



Max { Ci , Cj } 



(i) 



Where Qj is the number of session containing both pages i 
and j. Ci and Cj are respectively the number of sessions 
containing only pages i or page j. Dividing by the maximum 
between single occurrences of two pages has the effect of 
reducing the relative importance of links involving index 
pages. Such pages are those that generally do not contain 



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useful content and are used only a starting point for a 
browsing session. The edge weights (WPy) are kept in the 
adjacency matrix WA where each entry WAy contains the 
value computed according to equation (1). To limit the number 
of edge in such graph, elements of WA y whose value is less 
than a threshold are known to be less correlated and thus 
discarded. 

2) Clustering 



We apply a graph partitioning algorithm to find groups of 
strongly correlated pages by partitioning the graph according 
to its connected components. Clusters are formed by starting 
from a Vertex a DFS on the graph induced by WM is applied 
to search for the connected component reachable from this 
vertex. Once the component has been found, the algorithm 
checks if there are any nodes not considered in the visit. If it 
so, it means that a previously connected has been split and 
therefore, it needs to be identified. To do this, DFS is again 
performed by starting from one of the nodes not visited. In the 
worst case, when the entire URL in the same cluster, the cost 
of the algorithm will be linear in the number of edges of the 
complete graph G. Before the clusters are put into navigational 
pattern profile, the clusters are ranked based on values store in 
the WM matrix. It will be used for classification performed by 
foragers based on the Greatest Common Subsequence 
Detection algorithm and also used for knowledge eliciting. 

B. FrontEnd Phase of Architecture 

In the FrontEnd Phase of our system, URL request of 
the user is processed by the Onlooker Agent (OA) and Forager 
Agents (FA). In the case of multiple profitable outputs, the 
best option is choosen by the Intuition Deductive Inference 
Engine (IDIE) which is run by Onlooker Agent. Finally, 
captured imminent browsing pattern is suggested to the user 
who initiated the URL request to web server. 

a) Work of Onlooker and Forager agent in FrontEnd 

Phase 
The critical component of our system is Onlooker agent 
and Forager agent. Main inputs for these agents are 

1. Navigation pattern profile: It consists of clusters 
formed in the BackEnd Phase of our system. 

2. Live session window: A Sequence 
LSW={lwp 1 ,lwp 2 ,....,lwp m } is the current size of live session 
window where m is the size of the current active session 
window. 

On receiving URL's in the form of Live Session Window, 
Onlooker initiates the Foragers that correspond to number of 
clusters in the navigation profile. Each Forager agent (FA) 
acts on the Navigation profiles by executing the novel Greatest 
Common Subsequence Detection to discover the subsequence 
which may be considered as imminent browsing pattern of 
web user. Each FA submits the profitable score along the 
discovered subsequence to the onlooker. 

When the Onlooker Agent (OA) receives the profitable 
score along the sub sequences, it starts to decide the best 
profitable source of navigation profile. In the case of close 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
race between the sub sequences, Intuition deductive 
inference engine (IDIE) plays a crucial role in selecting the 
right cluster. The main objective of IDIE is to test the each 
cluster of discovered navigation pattern against already built 
knowledge base and choose the cluster which attains the 
maximum number of matches with the knowledge base. 
Finally, Onlooker suggests the output of IDIE as the imminent 
browsing pattern of web user. The cooperation between 
Onlooker Agent (OA) and Forager Agents (FA) is depicted in 
the Figure 4. The OA listens to FA. It is just similar to honey 
bee dancing area where Onlooker listens to dances of different 
foragers about the profitable food source. 



Live 
Session 
Window 




Intuition 

Deductive 

Inference Engine 

(IDIE) 



Cluster 1 



Cluster 2 



Cluster n 



User 



Captured 

forthcoming 

Navigation pattern 



Figure 4. Onlooker Agent and Forager Agents 

b) A Igorithm for predicting for the om ing navigation 
pattern of online users 

The following Algorithm 1 depicts the working behaviour 
of onlooker and forager agents to capture the forthcoming 
browsing pattern of user in the FrontEnd phase of the 
Architecture. 

Algorithm 1 

1 . Live Session Window (LSW) is given as input to the 

Onlooker Agent.LSW is the set of web page visited by 
user in the live session. LSW is represented as {LWP l9 
LWP 2; ..., LWP n ) where 'n' is the size of session 
window. 

2. Onlooker Agent (OA) initiates the number of Forager 
Agent (FA X ) that corresponds to each cluster in 
Navigation Profile (NP n ) where'n' is number of cluster. 



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(IJCSIS) International Journal of Computer Science and Information Security, 



•th 



FA t - Forager Agent works on i cluster in NP 
and i G n. 

3. Initially onlooker agent assigns the arbitrary profitable 
score of value 100 to each Forager Agent (FA;). The 
score is denoted as score(FAi).This score is updated by 
the respective Forager Agent on the discovery of 
subsequence in the step 4. 

4. For each Forager Agent on its assigned cluster of 
Navigation Profile does the following 

i. Each FAi executes the Greatest Common subsequence 
Detection (described in c) on its cluster in respect to 
live session window (LSW) which produces the 
highest degree of GCD as the discovered subsequence 
which could be the candidate of online user's imminent 
browsing pattern, 
ii. Discovered sequence is denoted as IBP = {IBP l9 IBP2,. 
., IBPn}.Each Forager Agent updates its initial score 
using Equation (2) and with the help of adjacency 
matrix WA built in the BackEnd Phase where each 
entry WA y contains the value computed according to 
equation (1). 



IF IPS, 



Vol 8, No. 3, 2010 



■PS 2 I or |PS 1 -PS 3 I or IPS^PSJ 



< P Where /? is Uncertain Profitable 
Threshold value then 

There is race between discovered subsequence of PSi or 
PS2 or PS 3 to become an imminent browsing pattern of web 
user. Onlooker Agent (OA) sends the cluster of competing sub 
sequences to the Intuition Deductive Inference Engine 
(IDIE).The main objective of IDIE (described later) is to test 
the each clusters navigation pattern against already built 
knowledge base and choose the cluster which attains the 
maximum number of matches with the knowledge base. 

ELSE 

Onlooker chooses the PSl's sequence as best 
discovered subsequence. 

6. Finally, Onlooker Agent reports the predicted 
forthcoming browsing pattern as PS x to user Or best 
Sequence selected by the IDIE in the case of 
competition. 

7. Suppose, if the next user activity in live session window 
different from the suggested captured list then the 
system has to restart once again to classify the new user 
activities. 

c) Greatest Common Subsequence Detection 



Ascore (FAi) = score(FAi) + J Z WA ibp iL w Pj 
Where WA 



(2) 



1=1 j=i 
mbhlwpj = Value in Adjacency 
matrix between the each page in Imminent Browsing 
Pattern (IBP) discovered by Forager and pages in the 
Live Session Window (LSW). 
iii. After each FAi executed the steps i and ii, Forager 
Agent sends its updated score and discovered 
Subsequence to the Onlooker Agent (OA). 
5. After receiving the profitable scores from each Forager 
agents (FA i)j It selects the first 3 High scored Forager 
Agent's output. The scores are denoted as PS l9 PS2 
and PS 3 . 

i. Onlooker Agent computes the absolute difference 
between the PS l9 PS2 and PS 3 to find the 
closeness. 



Every Forager Agent initiated by Onlooker Agent should 
perform the similarity comparison between set of pages in 
Live Session Window and web pages in the cluster to discover 
the subsequence that could be the forthcoming browsing 
pattern of online user. It's clear that every forager agent has to 
perform some kind of pattern matching. 

In the pattern matching [9], comparing the similarity between 

the two sequences a and b are fundamental problem. One 
of the fundamental problem is to determine^ the Greatest 

Common Subsequence (GCS) between a and b . The GCS is 
a String comparison metric that measures the subsequence of 
maximum length common to both the sequences. Main 
objective of Forager Agent (FA) is to find the Greatest 
Common Subsequence among the sequence of paths in the 

form of page visits A= {al,a2,a3,a4, an) , 

B={bl,b2,b3,b4,...bn). 

Theorem 1 Let a= {al,a2,a3,a4, an) and 

b={bl,b2,b3,b4,...bm) be the sequences and Let 
d={dl,d2,d3,...dn} be any GCD of a and b. 

1. If an=b m then di=a n =b m and d n _i is a GCD of a nA and 

bn-i- 

2. If an^b m then di^ implies d is a GCD of an.! and b. 

3. If an^b m then di^b m implies dn is a GCD of a and b m . L 



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We have implemented the GCD with added module that 
outputs the subsequence of indices of the two sequences that 
match in getting the Greatest Common subsequence. For 
example, If A={wpl,wp2,wp3,wp2,wp4,wp2,wpl} and 
B={wp2,wp4,wp3,wpl,wp2,wpl}. Their GCD is GCD= 
{ wp2,wp4,wp2,wp 1 ). 

d) Combined Effort of Greatest Common subsequence 
Detection and Intuition Deductive Inference Engine (IDIE) 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
against the knowledge base to find the semantic valid 
matching count. Intuition Deductive Inference Engine is 
designed to infer the facts by following Top Down inference 
mechanism. The excerpts of our IDIE knowledge base are 
shown in Table I.The fact Next (wp 7 (cse 
research),wp 6 (research)) says that wp 7 is the semantically the 
next page of wp6 and values in the bracket are ontological 
terms gathered from OM array. The rule (1) can directly apply 
on the facts where as rule (2) is recursive nature. 



After performing the clustering algorithm discussed in 
A.b.2 of BackEnd Phase, We have a set of cluster wnp = 
{wnp 1 ,wnp 2 ,wnp 3 ,...,wnp n } where wnp 1 ={wp 1 ,wp 2 ,...,wp k } is a 
set of K pages as a users navigational pattern for each 1< i < n. 
Here, wnp is set of web navigational pattern in the cluster. 

We used the navigation profile which has web 
navigational pattern of different cluster as facts for building 
the knowledge base of Intuition deductive Inference Engine. 
In addition, OA an ontology array data structure built in the 
Content retrieving step of BackEnd phase is used to form the 
meaningful facts. We used the unique ontological term for 
each web page in the site. We get the ontology term of each 
page from Meta tag of each page which conveys the actual 
content of the page. Also, Structure of Website is used to input 
the additional facts for knowledge base. In our WUM system, 
Knowledge base building is considered as critical part. 

A Sequence LSW={wp!,wp 2 ,....,wp m } is the current size of 
live session window where m is the size of the current active 
session window. Before Onlooker Agent initiates the Forager 
Agents to execute GCD on its cluster, we need to order the lsw 
sequence based on their adjacency weight matrix (WM) 
constructed in the navigation pattern modeling. Also, we rank 
all the clusters based on their weight values. Each cluster 
weight is computed as sum of all its edges weight. After this 
step, each Forager Agent initiated by OA with the arbitrary 
profitable score applies the Greatest Common Subsequence 
Detection on the assigned cluster in respect to live session 
window (LSW) which produces the highest degree of GCD. 
Each Forager Agent sends its updated score and discovered 
subsequence to the Onlooker Agent (OA). 

After receiving the profitable scores from each Forager 
agents (FA i)j OA selects the first 3 High scored Forager 
Agent's output and finds whether absolute difference between 
them lesser then /3 (Uncertain Profitable Threshold value). 

In the case of competition between discovered sequences by 
forager agents, Intuition deductive inference engine (IDIE) 

plays a crucial role in selecting the right cluster among various 
options. 

The main objective of IDIE is to test the each clusters 
navigation pattern against already built knowledge base and 
choose the cluster which attains the maximum number of 
matches with the knowledge base. The rules of knowledge 
base are written in InterProlog notation. Our inference engine 
runs on the facts to check how many of web page navigational 
sequence in each cluster are semantically correct. The rule 
base is written in such a way that it checks out each cluster 



As we discussed earlier, clusters of competing discovered 
sequences selected by Onlooker Agent (OA) is given as input 
to IDIE. The IDIE infers the count for each cluster that shows 
how many sub sequences in each cluster are semantically 
matching with the knowledge base. Finally, IDIE reports a 
cluster of maximum valid match count. This is used for 
preparing the intuition captured list and provided to the user as 
a recommendation. Suppose, if the next user activity in live 
session window different from the suggested captured list then 
onlooker agent has to restart the algorithm once again to 
identify the forthcoming user activities on their site. 



TABLE I. 



Excerpts of IDIE Knowledge Base 



Facts 

Next (wp 7 (cse research),wp 6 (research)) 
Next (wpio(it research),wp 6 ( research)) 
Next (wpi 7 (cse),wp 2 4(course)) 
Next (wp 7 (cse research),wpi 7 (cse)) 
Next (wp 27 (cse staff details),wpi 7 (cse)) 



Rules: 

Subsequence(x,y):- Next(x,y) (1) 

SuperSubsequence(x,y):- Next(x,z) , Next(z,y) 2).. 



IV. Illustration of Forager Agent Based System 

For our illustration, consider the navigation profile of 
BackEnd Phase as shown in Table II. Assume the Live 
Session Window size as 3 and set of web pages visited by user 
in the live session as LSW= {wp 3 7,wp 2 7,wp 18 }. As stated in 
our algorithm 1, LSW is given as input to the Onlooker Agent 
(OA). OA initialize FA, (FA 1? FA 2 , FA 3 , FA 4 and FA 5 ) that 
works respectively on Navigation Profile (NP 1? NP2, NP3, 
NP4 and NP 5 ) with the initial arbitrary profitable score of 
100. Each Forager Agent (FA,) executes the Greatest Common 
subsequence (GCD) on its own controlled cluster in respect to 
Live Session Window (LSW). 

While Forager Agent discovers the subsequence that could 
become Imminent Browsing Pattern (IBP), update its score 
according to equation (2) i.e. Sum of adjacency matrix values 
for pages between LSW and IBP is added to initial profitable 
score. Consider the Table III that depicts the score of each 
Forager Agent (Score (FA,)) and its discovered sequence 
(IBP^.Each Forager Agents (FA t ) sends its Final Profitable 
Score (FA t ) and Discovered Sequence (IBP^ to Onlooker 
Agent (OA). After receiving the profitable score from all 
initialized FA 1? OA selects the first three high scored FA L 



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In this example, only FA 3 and FA 5 produced the updated 
profitable score. The remaining Forager Agents were not 
updated their initial score. 



TABLE II. 



Navigation Profile of BackEnd Phase 



Navigation Profile 


Clustered Navigation Pattern 


NPi 


{wp 2 ,Wpio,Wpi5,Wp20,Wp 8 } 


NP 2 


{wp3,Wp 2 7,Wp 54 ,Wpioo,Wpi2l} 


NP 3 


{wp 2 ,Wpi9,Wp37,Wp27,Wp30,Wpi8,Wp 6 o} 


NP 4 


{wp 5 ,Wpi5,Wp 2 3} 


NP 5 


{wp 7 ,Wp37,Wp31,Wp27,Wp29,Wp26,Wpi 8 } 



table iii. score(fai) and its discovered sequence (ibpi) in 
respect to Live Session Window 



Forager 
Agents 

(FAO 


Initial 
Score 


Navigation 
Profile 
(NPi) 


Discovered 
Sequence (IBPi) 


Final 

Profitable 

Score 

(FAO 


FAi 


100 


NPi 


NO 


100 


FA 2 


100 


NP 2 


NO 


100 


FA 3 


100 


NP 3 


{wpi9,Wp 3 0,Wp 6 o} 


172 


FA 4 


100 


NP 4 


NO 


100 


FA 5 


100 


NP 5 


{wp 7 ,Wp 3 l,Wp 2 , 

wp 26 } 


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Vol 8, No. 3, 2010 
cluster which attains maximum valid match count in respect to 
LSW. In this example, FA 5 is reported by the IDIE for 
attaining the more number of valid matches with knowledge 
base than FA 3 . From the FA 5 , OA suggest the following list as 
perfect Imminent Browsing Pattern of user 

IBP = {wp 7 ,Wp 31 ,Wp29,Wp26}. 



V. Data Analysis Report 

Our proposed Forager Agent based system was tested on 
the web log dataset of Sona College. This site was deployed in 
the IBM's Web sphere Application Server. All the algorithms 
of BackEnd and FrontEnd phase were implemented in the 
JAVA. The knowledge base used in the IDIE of FrontEnd 
phase was implemented using the InterProlog. InterProlog 
provides us the ability to call prolog goal through a prolog 
object and for prolog to invoke any JAVA method through a 
Java Message Predicate. The BackEnd phase of our system 
was tested on the weblog entries of 120 students over a period 
of 8 weeks. There were approximately 52,745 entries in the 
log file. For our input dataset, BackEnd phase had produced 
the navigation profile output which consists of 15 clusters. The 
performance of the proposed system was analyzed based on 
the two metrics. They are namely Accuracy and Coverage. 

a) Accuracy based analysis of Forager Agent based 

System 
Accuracy measure is defined as a degree to which captured 
imminent browsing pattern as suggested by the system 
matches with the actual browsing pattern of user. It is given by 

\p(IBP„ r ,LSW)nOrizinaL 



NO- No Output. 

In the next step, Onlooker Agent (OA) finds the absolute 

difference between \Score(FA 3 ) - Score(FA 5 )| and 

compares the value with the /? (Uncertain Profitable 

Threshold value). Here the value of /3 is assigned to be 50. In 

this example, the value of \Score(FA 3 ) - Score(FA 5 )\ is 

41 which lesser than/?. This situation is the typical case of 
competition of who to become the Imminent Browsing pattern 
of user between IBP 3 and IBP 5 which are discovered sequence 
of FA 3 and FA 5 Now, Onlooker Agent uses the Intuition 
Deductive Inference Engine (IDIE) to choose the best one 
among the alternatives IBP 3 and IBP 5 

Onlooker Agent feds the clusters of competing discovered 
sequence NP 3 and NP 5 to the IDIE. The main function of IDIE 
is to check each page of clusters with knowledge base and find 
whether they are semantically valid next page in respect to 
pages in Live Session Window. Onlooker Agent chooses the 



Accuracy= 



\P(IBP m ,LSW) 



(3) 



LSW - Live Session Window P(IBP np ,LSW) - 

Navigation Pattern in predicted imminent browsing pattern of 

user. Original - Original Navigational pattern of user 

The Figure 5 depicts the accuracy of our Forager Agent 
based system as Live Session Window (LSW) size is 
increased. Our results show that increase of LSW size gives 
more wisdom to system that improves the accuracy. 

The Figure 6 depicts the comparison of our Forager Agent 
based system with the other two recommendation system 
namely Web personalizer and SUGGEST. Our results show 
that Our Forager Agent based system outperforms the 
recommendation system with the excellent behaviour of 
Forager Agents which uses Greatest Common Subsequence 
Detection to predict the forthcoming browsing pattern of user. 
In the case of Competition between alternative, Onlooker 
Agent uses the Intuition Deductive Inference Engine to choose 
the best one and thereby completely avoids the 
misclassification in finding the forthcoming browsing pattern 
compared with other system. Our Forager Agent based system 
outperforms the other recommendation system and achieves 
the accuracy of 92% compare to the Web personalizer (81%) 
and SUGGEST (83%) when Live size window is 10. 



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b) Coverage based analysis of Forager Agent based 

System 
Coverage measure is defined as the ability of two tier 
Forager agent based system to produce all page views that are 
most likely visited by the user. It is given by 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
inference engine to choose the best one among the 
alternatives. Finally, recommends the selected one as 
forthcoming navigation pattern of the user. The practical 
implementation shows that our approach really improves the 
accuracy of predicting the forthcoming browsing pattern of 
user and satisfies the users compared to other system. 



Coverage : 



\p(IBP„ r ,LSW)n Original 
\Original np \ 



(4) 



The Figure 7 depicts the coverage of our Forager Agent 
based system as Live Session Window (LSW) size is 
increased. The practical implementation of our proposed two 
tier Forager agent based system on sona college dataset prove 
that there is increase in the accuracy of predicting the 
navigation pattern of online user and also Coverage measure is 
excellent. 



Accuracy of Forager Agent based System 



100 
90 

-. 80 
£ 70 

2 60 

I 50 

40 

30 

20 

10 






92 



n 1 1 1 1 1 1 1 



3 4 5 6 7 
LSW Size 



9 10 



Figure 5. Accuracy of Forager Agent Systems 

VI. Conclusions 

Our proposed two tier Forager agent based system 
presented in this paper was inspired from onlooker bee making 
a decision of profitable food source using a Collective 
intelligence of Foraging Behaviour in Bee's Hive. In our 
system, fleet of forager is originated by onlooker agent on 
number of clusters formed in the BackEnd Phase. Each forager 
executes the Greatest Common Subsequence detection on its 
cluster of web pages and also runs the scoring algorithm to 
discover the subsequence that could be the imminent browsing 
pattern of user. Each forager agent sends the final score to 
onlooker agent. In case of more than one profitable cluster, it 
is the onlooker agent that runs the intuition deductive 



100 

90 

80 

70 

£ 60 

r> so 

2 40 

3 30 

< 20 

10 




Forager Agent based system Vs others 


Forager Agent 

Web 

personalizer 

SUGGEST 




^-r- 0000 * 000 --^ 


^^^JpLf^t^l 8i 


-H^IS™ *ns 


'~1& 59 













1 1 1 1 1 1 1 1 

3456789 10 
LSW Size 



Figure 6. Accuracy of Forager Agent based System Vs Other Systems 



90 
80 
70 
60 

V 50 

WD 

2 

t 40 

o 

30 

20 

10 




Coverage of Forager Agent based System 




^ 


^^S^ 


^^^ 56 












3456789 10 
LSW Size 



Figure 7. Coverage of Forager Agent based System 

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AUTHORS PROFILE 

V.Mohanraj received his ME Computer science and Engineering 
from Anna University, Chennai in 2004. He is currently working as 
Assistant professor in IT department of Sona College of Technology. 
He is pursuing PhD degree in Anna University, Chennai. His research 
area includes web mining, database and intelligent system. 

Dr.R Lakshmipathi received his BE from College of 
Engineering, Anna University, India, in 1971, the ME and PhD in 
Electrical Engineering from College of Engineering, Anna 
University, India in 1973 and Indian Institute of Technology (IIT), 
Chennai in 1979, respectively. He has 36 years of teaching 
experience at UG degree level out of which 10 years in PG degree 
level. He worked as principal in Govt. College of Engineering and 
held the various prestigious posts like Dean, Regional Research 
Director , Chairman for board of BE exams, Member of Academic 
auditing committee, AICTE, University and State Govt, expert 
committee member. He is currently a professor of Electrical 
Engineering, St. Peters Engineering College (Deemed University), 
Tamilnadu. His research interest includes Electrical power semi 
conductor drives, Signal processing and Web Mining. 

J.Senthilkumar received his ME Applied Electronics from Anna 
University, Chennai in 2004. He is working as assistant professor in 
IT Department of Sona College of Technology and pursuing PhD 
degree in Anna University, Chennai. He is active in the research area 
of data mining and Mobile computing. 

Y.Suresh received his ME Applied Electronics from Anna 
University, Chennai in 2004. He is working as assistant professor in 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
IT Department of Sona College of Technology and pursuing PhD 
degree in Anna University, Chennai. He is active in the research area 
of data mining, control system and Mobile computing. 



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Quality of Service Issues in Wireless 
Ad Hoc Network (IEEE 802.11B) 



Mohammed Ali Hussain 1 , Mohammed Mastan 2 , Syed Umar 3 

Research Scholar, Dept.of CSE, Acharya Nagarjuna University, Guntur, A.P., India. 

hussain_ma2k@ yahoo, coin 

2 Research Scholar, Dept.of CSE, JNT University, Kakinada, A.P., India. 

mastanmohd @ gmail.com 

3 Research Scholar, Dept.of CSE, Dravidian University, Kuppam, A.P., India. 

umar3 32 @ gmail.com 



Abstract — A wireless Ad-hoc network consists 
of wireless nodes communicating without the 
need for a centralized administration, in which 
all nodes potentially contribute to the routing 
process. In this paper, we report Fluctuations in 
channel quality effect the QoS metrics on each 
link and the whole end-to-end route. The 
interference from non-neighboring nodes affects 
the link quality. QoS is an essential component 
of ad-hoc networks. The most commonly studied 
QoS metrics are throughput, bandwidth, delay 
and jitter. Bandwidth is the QoS metric that has 
received the most attention in the QoS literature. 
The QoS requirements are typically met by soft 
assurances rather than hard guarantees from the 
network. Most mechanisms are designed for 
providing relative assurances rather than 
absolute assurances. 

Keywords: QoS, Ad-hoc, Throughput, 
Bandwidth, Delay, Jitter, 802.11. 

I. INTRODUCTION 

Wireless Ad-hoc network consists of 
wireless nodes communicating without 
the need for a centralized 
administration. The idea of such 
networking is to support robust and 
efficient operation ad-hoc wireless 
networks in which all nodes potentially 
contribute to the routing process, the 
fluctuations in channel quality effect 
the QoS metrics on each link and the 
whole end-to-end route. In ad-hoc 



networks, Quality of Service support is 
becoming an inherent necessity rather 
than an "additional feature" of the 
network. Wireless channel fluctuates 
rapidly and the fluctuations severely 
effect multi-hop flows. As opposed to 
the wired network, the capacity of the 
wireless channel fluctuates rapidly due 
to various physical layer phenomena 
including fading and multi-path 
interference. In addition, background 
noise and interference from nearby 
nodes further effect the channel 
quality. In ad-hoc networks, the end- 
to-end quality of a connection may 
vary rapidly as change in channel 
quality on any link may effect the end- 
to-end QoS metrics of multi-hop paths. 
The Packets contend for the shared 
media of the same stream at different 
nodes impacts the QoS metrics of a 
connection. Such contention arises as 
the wireless channel is shared by nodes 
in the vicinity. Interference effects are 



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pronounced in ad-hoc networks where 
typically a single frequency is used for 
communication in the shared channel. 
In Single hop infrastructured wireless 
networks frequency planning is mostly 
used where nearby base stations can be 
configured to function at different 
frequencies for reducing interference. 
Transmissions in the wireless media 
are not received correctly beyond the 
transmission range. But even beyond 
the transmission range, the remaining 
power may be enough to interfere with 
other transmission. So, interference 
from nonneighboring nodes may result 
in packet drops. In order to support 
QoS on multi-hop paths, QoS must be 
designed for the end-to-end path as 
well as for each hop. The physical and 
MAC layers are responsible for QoS 
properties on a single-hop. The routing 
layer is responsible for QoS metrics on 
an end-to-end route. 

II. OVERVIEW OF IEEE 802.1 1 
PHYSICAL LAYER 

One of the fundamental challenges in 
wireless networks is the continuously 
changing physical layer properties of 
the channel. The physical layer of 
802.11b can support multiple data 
rates. Depending on the channel 
quality the data rate can be altered to 
keep the bit error rate acceptable, as 



high data rates are also prone to high 
bit error rates. 

The 802.11b standard operates in the 
2.4 GHz band and supports 1, 2, 5.5 
and 11 Mbps. For efficient use of a 
multi-rate physical layer, there have 
been several algorithms proposed at 
the physical layer. One of the 
algorithm which is closely tied to the 
MAC layer is Opportunistic Auto Rate 
(OAR) for improving throughput in the 
presence of multi-rate links in ad-hoc 
networks. The key idea is to send 
multiple packets when the channel rate 
is higher. 

III. IMPORTANCE OF MEDIUM 
ACCESS LAYER 

The original IEEE 802.11 [1] standard 
specifies the physical layer and the 
medium access layer mechanisms and 
provides a data rate up to 2 Mbps. 
Further the standards IEEE 802.11b 
modifies the physical layer part of the 
standard and increases the maximum 
data rates to 11 Mbps and 54 Mbps 
respectively. In this paper we discuss 
the basic 802.11 MAC layer 
functionality called Distributed 
Coordination Function (DCF) for 
distributed access to the shared 
medium. DCF is a natural choice for 
ad-hoc networks, as there is no 
centralized controller such as an 



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access-point. However, PCF can 
support QoS metrics in single-hop 
wireless networks due to its centralized 
design. Both DCF and PCF are 
enhanced in the upcoming standard 
802. lie [2] that are designed for 
supporting QoS in WLANs. 

IV. 802.11 DISTRIBUTED 
COORDINATION 
FUNCTION (DCF) 

The DCF protocol attempts to provide 
equal access (in terms of number of 
packets) to all backlogged nodes that 
share a channel. In an ad-hoc network 
the throughput that a node obtains 
using DCF is a function of the number 
of neighbors that it has and the state of 
their queues (backlogged or not). 



SIF 



SIF 



SIF 



Sourc 



RT 



DATA 



CT 



AC 



NAV(C 



window used for backoffs. Initially cw 
is set to cwmin . In the chosen slot the 
node sends a MAC layer control 
packet called RTS (request-to-send), to 
the receiver. If the receiver correctly 
receives the RTS and is not deferring 
transmission, it responds with CTS 
(clear-to-send). This is followed by 
transmission of the data packet by the 
sender, and a subsequent 

acknowledgment from the receiver. 
The transmissions of these four packets 
are separated by short durations called 
SIFS (Short Inter-Frame Space). The 
SIFS allows time for switching the 
transceiver between sending and 
receiving modes. The sequence of 
transmission of these four packets. The 
MAC header of all these packets 
contains a "duration" field indicating 
the remaining time till the end of the 
reception of the ACK packet. Based on 
this advertisement, the neighboring 
nodes update a data structure called 
NAV (Network Allocation Vector). 
This structure maintains the remaining 
time for which the node has to defer all 
transmissions. 



Figure 1: IEEE 802.11 DCF 

Each node that has a packet to send 
picks a random slot for transmission in 
[0, cw], where cw is the contention 



If the packet transmission fails, the 
sender doubles its contention window 
(cw <-[2*cw-l]) and backs off before 
attempting a retransmission. The 
number of retransmissions is limited to 



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4 for small packets (including RTS 
packets) and 7 for larger (typically 
DATA) packets. If these counts are 
exceeded, the data packet is dropped 
and cw is reset to cw m i n if the data 
packet is successfully delivered, both 
the sender and the receiver reset cw to 
cw min . 

V. PROPOSED QOS SUPPORT USING DCF 
BASED SERVICE DIFFERENTIATION 

As it is difficult to provide absolute 
QoS guarantees, relative QoS 
assurance can be provided by service 
differentiation. However, to provide 
differentiated services, the 802.11 
protocol needs to be modified. [3] 
proposes three ways to modify the 
DCF functionality of 802. 1 1 to support 
service differentiation. The parameters 
that need to be modified to achieve 
service differentiation are. 
1. Backoff increase function'. Upon an 
unsuccessful attempt to send an RTS 
or a data packet, the maximum backoff 
time is doubled. More specifically the 
backoff time is calculated as follows: 

(Bac^pfftvme = [2 ( 2+l )xrand()] XSCot tW ie 

Where i is the number of consecutive 
backoffs experienced for the packet to 
transmitted. To support different 



priorities, the backoff computation can 
be changed as follows: 

(Bac^pffnme = [ Pj ( 2+l )xrand() ] XSfotttm 

where pj is the priority of node j 
2. DIFS: As shown in Fig.l, this is the 
minimum interval of time required 
before initiating a new packet 
transmission after the channel has been 
busy. To lower the priority of a flow 
we can increase the DIFS (Distributed 
Coordination Function Inter Frame 
Spacing) period for packets of that 
flow. However, it is difficult to find an 
exact relation between the DIFS period 
for a flow and its throughput. Fig. 2 
shows the different DIFS values and 
the corresponding relative priorities. 



Priority j+l 

(high) 


DIFS j+l 




















l' 


■s 
















Priority j 

(intermediate) 


DIFSj 




















defer 








Priority j-1 (low) 


DIFS j-1 










defer 





Figure 2: Service Differentiation using 
different DIFS values 

3. Maximum Frame Length: Channel 
contention using the DCF functionality 
is typically used to send a single frame. 
By using longer frames, higher 
throughput can be provided to high- 
priority flows. 



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VI. CONCLUSION 



REFERENCES 



In this paper, the QoS issues discussed 
at various networking layers for ad-hoc 
networks. The physical layer and the 
MAC layers are primarily responsible 
for QoS metrics on each link and the 
whole end-to-end route. The DCF 
functionality of 802.11 is being 
extended and specifically designed for 
QoS support in multi-hop networks. 
The algorithm which is needed to be 
adapted for use in multi-hop ad-hoc 
networks is Opportunistic Auto Rate 
(OAR) for improving throughput in the 
presence of multi-rate links in ad-hoc 
networks. 

QoS is currently an active research 
area in ad-hoc networks. However, 
there are several avenues that require 
further exploration for designing a QoS 
enabled ad-hoc network. For packets 
that traverse multiple hops, the end-to- 
end QoS is a function of the QoS 
metrics at each intermediate link. End- 
to-end QoS properties can be improved 
by designing a MAC layer that 
coordinates with other intermediate 
nodes on a multi-hop path. We find 
that QoS is an inherent component of 
ad-hoc networking and that there are 
several unsolved challenges that need 
to be addressed to design QoS enabled 
ad-hoc networks in future. 



[1] IEEE Std. 802.11. Wireless LAN Medium 
Access Control (MAC) and Physical Layer 
(PHY) Specifications, 1999. 

[2] S. Mangold, S. Choi, G. R. Hiertz, O. 
Klein and B. Walke. Analysis of IEEE 
802. lie for QoS Support in Wireless 
LANs .IEEE Wireless Communications 
Magazine, Special Issue on Evolution of 
Wireless LANs and PANs, Jul. 2003. 

[3] I. Aad and C. Caselluccia. Differentiation 
mechanisms for IEEE 802. 1 1 . In Proc. 
IEEE Infocom, volume 2, pages 594-602, 
1996. 

[4] A.Veres, A. T. Campbell, M.Barry, and L. 
H. Sun. Supporting service differentiation 
in wireless packet networks using 
distributed control. IEEE Journal on 
Selected Areas in Communications, 
October 2001. 

[5] B.Sadeghi,V.Kanodia,A.Sabharwal, andE. 
Knightly. Opportunistic Media Access for 
Multirate Ad- hoc Networks. In Proc. 
ACMMOBICOM, 2002. 



AUTHORS PROFILE 



Mohammed All Hussain 

received the Master's 
degree M.Sc Computer 
Science from Alagappa 
University in 2003. He 
received Master's degree 
M.Tech in Information 
Technology from Allahabad Deemed 
University in 2005. He received Ph.D. degree 
In Computer Science from Magadh University, 
Bihar, India in 2008. He is doing Post Doctoral 
degree in Computer Science & Engineering 
from Acharya Nagarjuna University, Guntur, 
Andhra Pradesh, India. He is currently an 
Associate Professor in the Department of 
Computer Science in Nimra College of 
Engineering & Technology, Vijayawada, 
Andhra Pradesh, India. He had published 
several papers in National and International 
Conferences & International Journals. His 
research interests are Wireless Networks with 
specialization in Quality of Service (QoS) in 
IEEE 802.11 Wireless LANs. & Ad-Hoc 
Networks. He is a member of IACSIT and 
ISTE. 




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1 



Mohammed Mastan 

received the Master's 
degree in Computer 
Applications from 

Kakatiya University, 

Warangal in 2006. He 
received Master' s degree 
in M.Tech Computer Science & Engineering 
from JNT University, Hyderabad in 2008. He 
is pursuing Ph.D. in Computer Science & 
Engineering from JNT University, Kakinada, 
Andhra Pradesh, India. He is currently as 
Asst.Professor in Department of Computer 
Science & Engineering in Nimra College of 
Engineering & Technology, Vijayawada, 
Andhra Pradesh, India. He has published 
several papers in National and International 
Conferences. His research interests are 
Computer Networks & Wireless Networks. 






^_L 



Syed Umar received the 
B.Tech degree Electronics 
and Communication 

Engineering from JNT 
University, Hyderabad in 
2003. He received 
Master's degree M.Tech 
in Computer Science & 
Engineering from JNT University, Hyderabad 
in 2008. He is pursuing Ph.D. in Computer 
Science from Dra vidian University, Kuppam, 
Chittoor Dist, Andhra Pradesh, India. He is 
currently an Asst.Professor in Department of 
Computer Science & Engineering in Nimra 
College of Engineering & Technology, 
Vijayawada, Andhra Pradesh, India. He had 
published several papers in National and 
International Conferences. His research 
interests are Computer Networks & Wireless 
Networks. 



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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 



A New Collaborative Web Recommendation Systems 
based on Association Rule Mining 



A. Kumar 

Research Scholar, Department of Computer Science & 

Engineering , Sathyabama University, Chennai, India 

email:akr2020av@ yahoo, in 



Dr. P. Thambidurai 

Principal, Perunthalaivar Kamarajar Institute of 

Engineering & Technology, Karaikal, India. 

email: ptdurai58@yahoo.com 



Abstract — Massive development of internet in recent years 
necessitate the development of recommender systems that turn 
out to be user friendly in web applications. Recommender 
systems make an effort to outline user preferences over items, 
and model the relation between users and items. There are two 
elemental approaches that can be applied when generating 
recommendations systems. They are content based web 
recommender system and the other is collaborative web 
recommender system. This proposed paper presents a method of 
developing a collaborative web recommendation systems using 
association rule mining. The association rules were applied to 
personalization based on web usage data. The method utilize 
apriori algorithm to generate association rules. In general 
association rule mining is a technique common in data mining 
that attempts to discover patterns of products that are 
purchased together. The greater part of web page recommender 
systems that were proposed earlier utilized collaborative 
filtering. Web Content Recommendation has been an active 
application area for Information Filtering, Web Mining and 
Machine Learning research. The future work explains some of 
the modifications using other algorithms to generate the 
association rules that can be adopted on existing web 
recommendation system to make them functionally more 
effective. In order to explore the performance of the proposed 
web recommendation system experiments were conducted on 
available dataset. The performance of the proposed approach is 
best illustrated by comparing it with K-nearest neighboring 
algorithm. 



Keywords — Association Rules, 
Collaborative Recommender System, 
Machine Learning, and Web Mining. 



Apriori Algorithm, 
Information Filtering, 



I. 



Introduction 



The extent of the Internet is getting larger and larger in 
modern years. Therefore it is obligatory that a user need to 
expend much time to select indispensable information from 
large amount of web pages created every day. Addressing this 
problem, several web page recommender systems are 
constructed which automatically selects and recommends web 
pages suitable for user's support. The majority of web page 
recommender systems that was proposed earlier utilized 
collaborative filtering [1], [2], and [3]. Collaborative filtering 
is often used in general product recommender systems, and 



consists of the following stages. The foremost stage in 
collaborative filtering is to analyze users purchase histories in 
order to extract user groups which have similar purchase 
patterns. Then recommend the products that are commonly 
preferred in the user's group [4]. 

In general the Recommender Systems (RS) uses the opinion 
of members of a community to facilitate individuals identify 
the information most likely to be interesting to them or 
pertinent to their needs. This can be achieved by drawing on 
user preferences and filtering the set of feasible options to a 
more manageable subset. Every Web Recommendation 
Systems have its own advantages and limitations [5]. 
Moreover the assignment of recommender systems is to 
recommend items that fit a user's taste, in order to help the 
user in selecting/purchasing items from a devastating set of 
choices. Such systems have immense importance in 
applications such as e-commerce, subscription based services, 
information filtering, web services etc. 

There are two fundamental approaches that can be applied 
when generating recommendations. Content based approaches 
profile users and items by identifying their characteristic 
features, such us demographic data for user profiling, and 
product information/descriptions for item profiling. The 
profiles are used by algorithms to unite user interests and item 
descriptions when generating recommendations [6]. Web 
Content Recommendation has been an active application area 
for Information Filtering, Web Mining and Machine Learning 
research. This proposed paper presents a method of developing 
a collaborative web recommendation systems using 
association rule mining. The method utilize apriori algorithm 
to generate association rules. It also explains some of the 
baseline algorithms that are used in developing the web 
recommendation systems. The future work explains some of 
the modifications using other algorithms to generate the 
association rules that can be adopted on existing web 
recommendation system to make them functionally more 
effective. 

The remainder of this is organized as follows. Section 2 
discusses various collaborative web recommendation systems 
that were earlier proposed in literature. Section 3 explains the 
proposed work of developing a web recommendation system 



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(IJCSIS) 



using association rules generated from apriori algorithm. 
Section 4 illustrates the results for experiments conducted on 
different dataset in evaluating the performance of the proposed 
web recommendation system. Section 5 concludes the paper 
with fewer discussions. 

II. Related Work 

In general, the collaborative recommendation systems can 
be grouped into four categories. On the basis of its temporal 
and spatial characteristics, each system can be either 
synchronous or asynchronous, and either local or remote. 
Conversely, the most significant difference between these 
different collaborative Web recommendation systems is the 
method used to extort user preferences from personal 
information. This section of the paper discusses various 
methods proposed earlier in literature for a collaborative web 
recommendation system. 

Chen et al. in [7] proposed a Gradual Adaption Model for a 
Web recommender system. The model is used to track users' 
center of attention and its transition by analyzing their 
information access behaviors, and recommend appropriate 
information. The web pages admittance by users are classified 
by the concept classes, and grouped into three terms of short, 
medium and long periods, and two categories of significant 
and incomparable for each concept class, which are used to 
describe users' focus of interests, and to institute reprocess 
probability of each concept class in each term for each user by 
Full Bayesian Estimation as well. According to the reuse 
probability and period, the information that a user is likely to 
be interested in is recommended. They proposed a new 
approach by which short and medium periods are determined 
based on dynamic sampling of user information access 
behaviors. 

Niwa et al. in [8] described a web page recommender 
system based on Folksonomy mining. They projected a way 
to assemble a new type of web page recommender system 
covering all over the Internet, by using Folksonomy and 
Social Bookmark which are getting very well-liked in these 
days, a new way to express users' preference to web pages 
was formulated by mining tag data of Folksonomy. 
Folksonomy is a new classification technique which may take 
place of past taxonomy. Social Bookmark (SBM) is a variety 
of web services on which users can divide up their 
bookmarks. Anyone can see anyone's bookmark on SBM. In 
order to solve some problems faced by conventional 
recommender systems, they expressed users' web page 
preference by "affinity level between each user and each tag." 
By this approach, users' preferences are abstracted and it 
becomes easier to find similar users. Clustering can also solve 
the problem of "tag redundancy in Folksonomy." 

A hybrid web recommender system was described by 
Taghipour et al. in [9]. They exploit an idea of combining the 
conceptual and usage information to enhance a reinforcement 
learning framework, primarily devised for web 
recommendations based on web usage data. Moreover the 
combination can improve the quality of web 
recommendations. A hybrid web recommendation method is 



International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
proposed by making use of the conceptual relationships 

among web resources to derive a novel model of the problem, 

enriched with semantic knowledge about the usage behavior. 

With their proposed hybrid model for the web page 

recommendation problem they revealed the pertinent and 

flexibility of the reinforcement learning framework in the web 

recommendation domain, and demonstrated how it can be 

extended in order to incorporate various sources of 

information. Their test results suggested that the method can 

improve the overall quality of web recommendations. 

An intelligent recommender system was projected by 
kavitha devi et al. in [10]. They designed and implemented 
an Intelligent Collaborative Recommender System (ICRS) to 
map users' needs to the items that can persuade them. A 
methodology is used to animatedly modernize the accuracy 
factor based on user intelligence. The diverse approaches for 
recommendation are categorized as memory-based and 
model-based approaches. Memory-based systems suffer from 
data sparsity and scalability problems, whereas model-based 
approaches are liable to bind the range of users. Therefore 
they integrated these approaches to overcome their 
limitations. They applied the collaborative filtering approach 
for recommendations. Recommendations are made more 
accurate by applying regression to weighted aggregated 
predictions. Mean Absolute Error Metrics was considered for 
evaluating the performance of their proposed system. This 
approach thus alleviates scalability and sparsity problems and 
offers accurate recommendations. 

Cheng et al. in [11] developed a two stage collaborative 
recommender system. They proposed a chronological pattern 
based collaborative recommender system that predicts the 
customer's time- variant acquisition behavior in an e- 
commerce environment where the customer's purchase 
patterns may change gradually. A new two-stage 
recommendation process is developed to envisage customer 
behavior for the selection of different categories, as well as 
for product items. Their study is the first to recommend time- 
decaying sequential patterns within a collaborative 
recommender system. Their experimental results revealed that 
the proposed system outperforms the traditional collaborative 
system. 

Lin et al. in [12] described an efficient Adaptive -Support 
Association Rule Mining for Recommender Systems. They 
investigated the utilization of association rule mining as an 
underlying technology for collaborative recommender 
systems. Association rules have been used with sensation in 
other domains. Nevertheless, most currently existing 
association rule mining algorithms were designed with market 
basket analysis in mind. They described a collaborative 
recommendation technique based on a novel algorithm 
distinctively designed to excavate association rules for this 
rationale. The main advantage of their proposed approach is 
that their algorithm does not require the minimum support to 
be specified in advance. Rather, a target range is given for the 
number of rules, and the algorithm adjusts the minimum 
support for each user in order to obtain a rule set whose size is 
in the desired range. Moreover they employed associations 
between users as well as associations between items in 
making recommendations. The experimental evaluation of a 



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system based on their algorithm revealed that its performance 
is significantly better than that of traditional correlation-based 
approaches. 

Jung in [13] described a user-support method based on the 
distribution of knowledge with other users through the 
collaborative Web browsing, focusing exclusively on the 
user's interests extracted from users' own bookmarks. More 
prominently, they focused on those items of information 
which are associated to the user's interests. In collaborative 
Web browsing, they considered that recognizing the user's 
interests is an extremely essential mission. Furthermore, 
asking applicable information for other users, filtering the 
query results, and recommending them are additional most 
important tasks that have to be unreservedly conducted by 
them in [13]. Based on the personalized TF-IDF proposal they 
introduced the extended application of a BIS Agent, which is 
a bookmark sharing agent system. Moreover they 
implemented an ontological supervisor which can perform the 
semantic analysis of the Web sites pointed to by these 
bookmarks. They also designed a multi-agent system that 
consists of a facilitator agent and many personal agents. The 
main limitation of this system is that it does not consider the 
privacy problems related with sharing personal information of 
the user. 

A novel recommender system was formulated by Marko et 
al. in [14]. Their approach named, "Fab" is a recommendation 
system designed to help users sift through the mammoth 
amount of information obtainable in the World Wide Web. 
Their proposed approach is the combination of content-based 
filtering and collaborative filtering methods. The combination 
exploits the advantages of both the methods thereby avoiding 
the shortcomings. Fab's hybrid structure allows for automatic 
recognition of emergent issues relevant to various groups of 
users. It also enables two scaling problems, pertaining to the 
rising number of users and documents, to be addressed. In 
general the content-based approach to recommendation has its 
pedigree in the information retrieval (IR) community, and 
utilizes many of the same techniques. The collaborative 
approach computed the similarity of the users rather than 
computing the similarity of the items. They maintained user 
profiles content analysis and directly compared these profiles 
to determine similar users for collaborative recommendation. 
The process of recommendation can be partitioned into two 
stages: collection of items to form a manageable database or 
index, and subsequently selection of items from this database 
for particular users. The experimental results using the hybrid 
Fab system achieved higher accuracy. 

III. Proposed Approach 
A. Association Rule Mining 

In general association rule mining is a technique common 
in data mining that attempts to discover patterns of products 
that are purchased together. The proposed approach adapts the 
Apriori algorithm [15] to collaborative filtering in an attempt 
to discover patterns of items that have common ratings. 
Association rules capture relationships among items based on 
patterns of co-occurrence across transactions. The association 
rules were applied to personalization based on web usage data 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
in [16]. Considering each user profile as a transaction, it is 

possible to use the Apriori algorithm [15] and generate 

association rules for groups of commonly liked items. 

Given a set of user profiles U and a set of item sets I = {I l9 
I 2 ... Ik}, the support of an item set Ij e I is defined as g(Ij) = 
l{u g U : Ij c u)l / IUI. Item sets that satisfy a minimum 
support threshold are used to generate association rules. These 
groups of items are referred to as frequent item sets. In 
addition, association rules that do not satisfy a minimum lift 
threshold are shortened. If there is not adequate support for a 
particular item, that item will never come into view in any 
recurrent item set. The suggestion is that such an item will 
never be recommended. The subject of coverage is a tradeoff. 
Lowering the support threshold will make sure that more 
items can be recommended, but at the hazard of 
recommending an item without enough evidence of a pattern. 

Ahead of performing association rule mining on a 
collaborative filtering dataset, it is indispensable to discretize 
the rating values of each user profile. Therefore first subtract 
each user's average rating from the ratings in their profile to 
attain a zero -mean profile. Next, give a discrete category of 
"like" or "dislike" to each rated item in the profile if it's 
rating value is > or < zero, respectively. As a result of 
discretizing the dataset the total number of features used in 
the analysis is doubled. It is clear that a collaborative 
recommender must take such preference into description or 
risk recommending an item that is rated often, but disliked by 
consensus. Another possibility is that one association rule 
may add item 'i' to the candidate set with a "like" label, while 
another rule may add the identical item with a "dislike" label. 
There is not an ultimate solution in this case, but we have 
chosen to presuppose that there are opposing forces for the 
recommendation of the item. This implementation subtracts 
the confidence value of the "dislike" label from the 
confidence value of the "like" label. The search for item sets 
is facilitated by storing the frequent item sets in a directed 
acyclic graph, called a Frequent Item set Graph [16]. 

Given a objective user profile V, it is necessary to execute 
a depth-first search of the Frequent Item- set Graph. When we 
arrive at a node whose repeated item set I n is not enclosed in 
V, the item i e I n not found in V is added to the candidate 
set 'C and search at the current branch is finished. Note that 
the item set of the parent node I p to I n must be restricted in V 
by definition, and because I n is of size d + 1 where I p is size 
'd', there can be only one item i e I n that is not contained in 
V. It follows that I n = I p u {i} and the two nodes correspond 



to the rule L 



[ij. Then calculate the confidence of the rule 



as a(I n )/a(I p ). The candidate i e C is stored in a hash table 
along with its confidence value. If it already exists in the hash 
table, then the highest confidence value takes precedent. 

After finishing point of the depth-first search, all 
promising candidates for the target user V are enclosed in 
'C, including items labeled "dislike". Consecutively to 
accurately characterize an estimated negative implication, 
items labeled "dislike" is given a recommendation score that 
is the negation of the confidence value. As a final step, the 



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(IJCSIS) 



candidate set 'C is arranged according to the 
recommendation scores and the top N items are returned as a 
recommendation. 

B. Baseline Algorithms 

K Nearest Neighbour 

The customary k-nearest neighbor algorithm is broadly 
used and reasonably precise [17]. Resemblance is calculated 
using Pearson's correlation coefficient, and the k most 
analogous users that have rated the target item are selected as 
the neighborhood. This make note that a target user may have 
a different neighborhood for each target item. It is also 
general to filter neighbors with similarity below a specified 
threshold. This prevents prophecy being based on very remote 
or negative correlations. After identifying a neighborhood, 
Resnick's algorithm is used to calculate the prediction for a 
target item T and target user 'u'. 
K Means Clustering 

A standard model-based collaborative filtering algorithm 
uses k-means to cluster similar users. Given a set of user 
profiles, the space can be partitioned into k groups of users 
that are close to each other based on a measure of similarity. 
The discovered user clusters are then applied to the user based 
neighborhood formation task, rather than individual profiles. 
To make a recommendation for a target user 'u' and target 
item T it is essential to select a neighborhood of user clusters 
that have a rating for T and whose aggregate profile is most 
similar to 'u'. This neighborhood represents the set of user 
segments that the target user is most likely to be a member, 
based on a measure of similarity. Pearson's correlation 
coefficient is implemented effectively to perform this task. 
Probabilistic Latent Semantic Analysis 

In general the Probabilistic latent semantic analysis (PLSA) 
models [18] present a probabilistic approach for characterizing 
latent or hidden semantic associations among co -occurring 
objects. PLSA can be applied to the creation of user clusters 
based on web usage data. The proposed approach has adapted 
this technique to the context of collaborative filtering [19]. 
Moreover the Expectation-Maximization (EM) algorithm is 
used to perform maximum likelihood parameter estimation. In 
the expectation step, posterior probabilities are computed for 
latent variables based on current estimates. In the 
maximization step, Lagrange multipliers [20] are used to 
obtain the re-estimated parameters. Therefore iterating the 
expectation and maximization steps monotonically increases 
the total likelihood of the observed data L (U, I), until a local 
optimum is reached. 

IV. Experimental Results 

In order to evaluate the robustness of our recommendation 
algorithm based on association rule mining a data set is taken 
into account. This dataset consists of 100,000 ratings on 1682 
movies by 943 users. All ratings are integer values between 
one and five, where one is the lowest (dis-liked) and five is 
the highest (liked). Initially the accuracy of the proposed 
association rule mining based recommender system is 



International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
analyzed. To estimate the recommendations, 10-fold cross- 
validation on the entire dataset and without attack profiles 
was performed. Since Apriori selects recommendations from 
only among those item sets that have met the support 
threshold, it will by necessity have lower coverage than our 
baseline algorithms. There will be some items that do not 
appear in the Frequent Item set Graph, and about which the 
algorithm cannot make any predictions. This may the issues 
that arise in most of the baseline algorithms. 

The Apriori algorithm would therefore lend itself best to 
scenarios in which a list of top recommended items is sought, 
rather than a rating prediction scenario in which the 
recommender must be able to estimate a rating for any given 
item. The selectivity of the algorithm may be one reason to 
expect it will be relatively robust - it will not make 
recommendations without evidence that meets the minimum 
support threshold. However, the performance of Apriori and 
PLSA are superior to k-means at large attack sizes. 
Robustness of the Apriori algorithm may be moderately due 
to lower coverage. However, this does not account for the flat 
trend of hit ratio with respect to attack size. 

Only the Apriori algorithm holds steady at large filler sizes 
and is essentially unaffected. As with attack size, the reason 
that filler size does not affect the robustness of the algorithm 
is because adding more filler items does not change the 
probability that multiple attack profiles will have common 
item sets. The fact that a profile's ratings are discretized to 
categories of "like" and "dislike" means that an attack profile 
with 100% filler size will cover exactly half of the total 
features used in generating frequent item sets. Therefore, it is 
very unlikely that multiple attack profiles will result in mutual 
reinforcement. Apriori has also exhibited improved 
robustness compared to the other algorithms against certain 
attacks. 

The Apriori algorithm appears to have the same robustness 
as the other model-based algorithms at small attack sizes. 
Although the performance of Apriori is not ideal against a 
segment attack, certain scenarios may minimize the 
performance degradation in practice. In particular, a 
recommender system with a very large number of users is 
somewhat buffered from attack. The algorithm is quite robust 
through a 5% attack, and is comparable to both k-means and 
PLSA. The robustness of Apriori is not drastically reduced 
until attack size is 10% or greater. Table 1 shows the results 
of normalized mean absolute error evaluated for proposed 
approach and K nearest neighbor. Similar table 2 represents 
the coverage comparison of the proposed approach using 
apriori algorithm and K nearest neighbor. Figure 1 (a) shows 
the comparison of the proposed approach and k-nn algorithm 
in terms of their mean absolute error. Figure 1 (b) represents 
the comparison of apriori and k-nn algorithm in terms of 
coverage. The results revealed that the proposed approach 
using association rule mining performed well in 
recommendation by determining the user' needs. 



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Table.1. Normalized mean absolute error 



Approach 


Runl 


Run 2 


Run 3 


Mean 


Apriori 


0.3293 


0.3292 


0.3287 


0.3291 


K-nn 


0.3332 


0.3337 


0.3339 


0.3336 



Table. 2. Coverage 



Approach 


Runl 


Run 2 


Run 3 


Mean 


Apriori 


0.4701 


0.4716 


0.4718 


0.4712 


K-nn 


0.9942 


0.9941 


0.9942 


0.9942 



Normalized Mean Absolute Error 



0.335 
0.334 
0.333 
0.332 
0.331 
0.33 
0.329 
0.328 
0.327 
0.326 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
large amount of web pages created every day. Addressing this 

problem, several web page recommender systems are 

constructed which automatically selects and recommends web 

pages appropriate for user's support. The greater part of web 

page recommender systems that were proposed earlier utilized 

collaborative filtering. This proposed paper presents a method 

of developing a collaborative web recommendation systems 

using association rule mining. In general association rule 

mining is a technique common in data mining that attempts to 

discover patterns of products that are purchased together. The 

association rules were applied to personalization based on 

web usage data. The method utilize apriori algorithm to 

generate association rules. The Apriori algorithm would 

therefore provide itself best to scenarios in which a list of top 

recommended items is required, rather than a rating prediction 

scenario in which the recommender must be able to 

approximate a rating for any given item. The results revealed 

that the proposed approach using association rule mining 

performed well in recommendation by determining the user' 

needs. Future work mainly concentrates on determining the 

mutual reinforcement between common item sets thereby 

ehnancing the accuracy of the recommender system. 



- Apriori 
-K-nn 



Run 1 



Run 2 



Run 3 



(a) 







Coverage 


1.2 - 

1 _ 














0.8 - 
0.6 - 
0.4 - 














— ♦ — Apriori 
-■—K-nn 


+ 


^ a 














0.2 - 












- 










i 




Run 1 


Run 2 Run 3 



(b) 



Figure. 1 (a) & (b) represents comparison of Apriori and K-nn 

Algorithm in terms of Normalized Mean Absolute Error and 

Coverage respectively 

V. Conclusion 

The scope of the Internet is getting larger and larger in 
recent years. Therefore it is compulsory that a user need to 
disburse much time to decide on necessary information from 



References 



[i] 



J. Li, and O. Zaiane, "Combining Usage, Content, and Structure Data to 
Improve Web Site Recommendation," Proceedings of WebKDD-2004 
workshop on Web Mining and Web Usage, In 5th International 
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315,2004. 

P. Kazienko, and M. Kiewra, "Integration of relational databases and 
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Symposium on Database Engineering and Applications Symposium, 
IDEAS '04, pp. 111-116,2004. 

N. Golovin, and E. Rahm, "Reinforcement Learning Architecture for 
Web Recommendations," Proceedings of the International Conference 
on Information Technology: Coding and Computing (ITCC04), vol. 2, 
p. 398, 2004. 

Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John 
T. Riedl, "Evaluating collaborative filtering recommender systems," 
ACM Transactions on Information Systems, vol. 22, no. 1, pp. 5-53, 
2004. 

Punam Bedi, Harmeet Kaur, and Sudeep Marwaha, "Trust based 
Recommender System for the Semantic Web," Proceedings of the 20 th 
international joint conference on Artificial intelligence, pp. 2677-2682, 
2007. 

Gabor Takacs, Istvan Pilaszy, Bottyan Nemeth, and Domonkos Tikk, 
"Scalable Collaborative Filtering Approaches for Large Recommender 
Systems," The Journal of Machine Learning Research, vol. 10, pp. 623- 
656,2009. 

Jian Chen, Roman Y. Shtykh, and Qun Jin, "A Web Recommender 
System Based on Dynamic Sampling of User Information Access 
Behaviors," Ninth IEEE International Conference on Computer and 
Information Technology, vol. 2, pp. 172-177, 2009. 
Satoshi Niwa, Takuo Doi, and Shinichi Honiden, "Web Page 
Recommender System based on Folksonomy Mining," Proceedings of 
the Third International Conference on Information Technology: New 
Generations, IEEE Computer Society, pp. 388-393, 2006. 
Nima Taghipour, and Ahmad Kardan, "A hybrid web recommender 
system based on Q-learning," Proceedings of the 2008 ACM 
symposium on Applied computing, pp. 1164-1168, 2008. 
[10] M.K. Kavitha Devi, and P. Venkatesh, "ICRS: an intelligent 
collaborative recommender system for electronic purchasing," 



[2] 



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[4] 



[5] 



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[9] 



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International Journal of Business Excellence 2009, vol. 2, no. 2, pp. 

179-193, 2009. 
[11] Cheng-Lung Huang, and Wei-Liang Huang, "Handling sequential 

pattern decay: Developing a two-stage collaborative recommender 

system," Elsevier, Electronic Commerce Research and Applications, 

vol. 8, no. 3, pp. 117-129,2009. 
[12] Weiyang Lin, Sergio A. Alvarez, and Carolina Ruiz, "Efficient 

Adaptive-Support Association Rule Mining for Recommender 

Systems," Journal on Data Mining and Knowledge Discovery, vol. 6, 

no. 1, pp. 83-105,2004. 
[13] Jason J. Jung, "Collaborative Web Browsing Based on Semantic 

Extraction of User Interests with Bookmarks," Journal of Universal 

Computer Science, vol. 11, no. 2, pp. 213-228, 2005. 
[14] Marko Balabanovic, and Yoav Shoham, "Fab: content-based, 

collaborative recommendation," Communications of the ACM, vol. 40, 

no. 3, pp. 66-72, 1997. 
[15] R. Agrawal, and R. Srikant, "Fast algorithms for mining association 

rules," In Proceedings of the 20th International Conference on Very 

Large Data Bases (VLDB'94), Santiago, Chile, September 1994. 
[16] M. Nakagawa, and B. Mobasher, "A hybrid web personalization model 

based on site connectivity," In Web KDD Workshop at the ACM 

SIGKKDD International Conference on Knowledge Discovery and Data 

Mining, Washington, DC, August 2003. 
[17] J. Herlocker, J. Konstan, A. Borchers, and J. Riedl, "An algorithmic 

framework for performing collaborative filtering," In Proceedings of the 

22nd ACM Conference on Research and Development in Information 

Retrieval (SIGIR'99), Berkeley, CA, August 1999. 
[18] T. Hofmann, "Probabilistic latent semantic analysis," In Proceedings of 

the Fifteenth Conference on Uncertainty in Artificial Intelligence, 

Stockholm, Sweden, July 1999. 
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filtering as a defense against profile injection attacks," In Proceedings 

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[20] T. Hofmann, "Unsupervised learning by probabilistic latent semantic 

analysis," Machine Learning Journal, vol. 42, no. 1, pp. 177-196, 2001. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No. 3, 2010 
research interests include Natural Language Processing and 

Data Compression. He has delivered lectures in the areas of 

NLP, Information Security and Image Compression in 

conferences. He organized and acted as a chair person for 

various seminars and conferences. He introduced many 

research programmes in Pondicherry University. He is a life 

member of Computer Society of India and Indian Society for 

Technical Education. 



Author Biography 

r^^^^^H A. Kumar has around 21 years of 
J^^^ experience in Information Technology 

1 and its Applications with expertise in 

Data mining, Information and 
Knowledge Management and Web 
Technology. He has published a number 
of papers in peer-reviewed journals and 
conferences. He has actively served as a reviewer for many 
leading International Journals and conferences. He has 
delivered lectures in the areas of Web Recommendation 
Systems, Knowledge Management for Seminars and 
conferences. Currently serving as Head in Computer 
Science and Engineering, Perunthalaivar Kamarajar Institute 
of Engineering and Technology, India and Pursuing Ph.D., 
in the area of Web Recommendation Systems at 
Sathyabama University, Chennai. 



Dr. P. Thambidurai is Principal, 
Perunthalaivar Kamarajar Institute of 
Engineering & Technology, Karaikal, India. 
He received the M.E (Computer Science)., 
from College of Engineering, Guindy, 
Chennai and Ph.D. in Computer Science 
from Alagappa University, Karaikudi, India 
in 1984 and 1995 respectively. He has published more than 
100 papers in peer-reviewed journals and conferences. His 




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(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No.3, June 2010 



SIMILARITY BASED IMPUTATION 
METHOD FOR TIME VARIANT DATA 



F.Sagayaraj Francis, Vishnupriya.B, Vinolin Deborah Delphin, Saranya Kumari Potluri 
Pondicherry Engineering College, Pondicherry, India 



Abstract — The intent of any analysis is to make valid inferences 
regarding a population of interest. Missing data threatens this 
goal if it is missing in a manner which makes the sample different 
than the population from which it was drawn, that is, if the 
missing data creates a biased sample. Therefore, it is important 
to respond to a missing data problem in a manner which reflects 
the population of inference. This paper deals with the proposal of 
an efficient method of filling missing data called Similarity based 
Imputation Method (SIM). SIM processes the target segment by 
extracting its features and based on the extracted features the 
target segment is classified into its appropriate cluster which has 
complete data segments, which are similar to the target segment. 
Now from the similar segments within the identified cluster, the 
most identical segment is found using similarity measure and the 
values substituted from that complete segment 

Keywords— Imputation, Time variant Multi-dimensional data, 
Clustering, Feature Extraction, Similarity measure, Segment 
Matching 

I. Introduction 

A time series may be defined as a collection of 
readings belonging to different time periods, of some 
economic variable or composite of variables. It is a set of 
observations of a variable usually at equal intervals of time 
where time may be yearly, monthly, weekly, daily, hourly or 
even minute data. Hourly temperature reading, daily sales, 
monthly production, Earth's magnetic field variations are 
examples of time series. Time series analysis comprises 
methods for analyzing time series data in order to extract 
meaningful statistics and other characteristics of the data. 

The primary purpose of the analysis of time series is 
to discover and measure all types of variations which 
characterize a time series. For efficient analysis of data, 
complete datasets are required. There is a possibility to miss 
out several observations due to unexpected events such as 
equipment failure or unexpected disturbances. Moreover, 
occurrence of missing observations in datasets is an actual yet 
challenging issue confronted in machine learning and data 
mining. Missing values may generate bias and affect the 
quality of the supervised learning process or the performance 
of classification algorithms. However, most learning 
algorithms are not well adapted to some application domains 
due to the difficulty with missing values (for example, Web 
applications) as most existing algorithms are designed under 
the assumption that there are no missing values in datasets. 
That implies that a reliable method for dealing with those 
missing values is necessary. Generally, dealing with missing 
values means to find an approach that can fill them and 



maintain the original distribution of the data as closely as 
possible. There are many approaches to deal with missing 
values described in, for instance: 

1. Ignore objects containing missing values; 

2. Fill the missing value manually 

3. Substitute the missing values by a global constant or 
the mean of the objects 

4. Obtain the most probable value to fill in the missing 
values 

The first approach usually loses too much useful 
information, whereas the second one is time consuming and 
expensive and hence infeasible in many applications. The third 
and fourth approaches assume that all missing values are with 
the same value, probably leading to considerable distortions in 
data distribution. The method of imputation, however, is a 
popular strategy. In comparison to other methods, it uses as 
much information as possible from the observed data to 
predict missing values. 

The approach used in this paper fills the missing data 
by imputation using a similarity measure. The method 
involves finding a similar segment that matches the segment 
with missing data best using similarity measures of the 
segments. 

II. Existing Methodologies 

In general there can be two approaches to handle 
missing data. The first approach to find missing or lost data is 
by computation. These methods are statistical or numeric in 
nature, such as Line/Parabola of best fit or 
Interpolation/Extrapolation. The methods heavily depend on 
the formation of a function/model/system to fill missing data. 
The formation of function/model/system is inherently complex 
and requires voluminous computations. 

The second approach to find missing or lost data is by 
imputation. Imputation is the substitution of missing values in 
an incomplete dataset (target dataset) with values from a 
similar but complete dataset (source dataset). This approach 
uses similarity/distance/entropy measures to solve the 
problem. They can also be extended to predict patterns that are 
likely to occur. Traditional missing value imputation 
techniques can be roughly classified into two types. 

• Parametric imputation (e.g., the linear regression) and 

• Non-parametric imputation (e.g., non-parametric 
kernel-based regression method, Nearest Neighbor 
method). 

The parametric regression imputation is superior if a 
dataset can be adequately modeled parametrically, or if users 



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can correctly specify the parametric forms for the dataset. For 
instance, the linear regression methods usually can treat well 
the continuous target attribute, which is a linear combination 
of the conditional attributes. However, when the actual 
relation between the conditional attributes and the target 
attribute is not known, the performance of the linear regression 
for imputing missing values is very poor. In real application, if 
the model is incorrectly specified, the estimations of 
parametric method may be highly biased and the optimal 
control factor settings may be miscalculated. Non-parametric 
imputation algorithms that can provide superior fit by 
capturing structure in the dataset if the actual distribution of a 
dataset is known. Using a non-parametric algorithm is 
beneficial when the form of relationship between the 
conditional attributes and the target attribute is not known a- 
priori. 

While nonparametric imputation method is of low- 
efficiency, the popular nearest neighbor (NN) methods face 
two issues: (i) Each instance with missing values requires the 
calculation of the distances from it to all other instances in a 
dataset; and (ii) There are only a few random chances for 
selecting the nearest neighbor. 

A. Feature Extraction 

Time variant datasets are voluminous and hence 
handling raw time variant data is not viable. A solution to this 
would be to extract features from time series segments and 
deal with features rather than the raw data as such. 

Generalized feature extraction relies on a collection 
of feature extractors that function independently of domain 
and application. For time series data, such feature extractors 
must be able to identify generally useful structures that emerge 
from the relationships between consecutive measurement 
values over time. 

For traditional tabular data the similarity is often 
measured by attribute value similarity or even attribute -value 
equality [1]. For more complex data, e.g., geo- time series 
data, such simple similarity measures do not perform very 
well. So similarity of time series data should be based on 
certain characteristics of the data rather than on the raw data 
itself. Feature Extractor is characterized by its ability to 
capture fundamental trends and relationships, generate 
accurate approximations, represent the extracted structures 
compactly, support subsequent classification, and being 
domain independent [2]. But this technique only deals with 
finding the nature of a particular segment and does not extract 
the uniqueness of each segment and it does not maintain 
originality of the target segment. Therefore a more general 
time series feature extraction technique is to be adopted which 
would represent any time series. 

A well established feature extraction technique using 
Discrete Fourier Transformation (DFT) for time series use 
only the first k coefficients, discarding the rest. This 
corresponds to saving only a rough sketch of the time series, 
because the first coefficients represent the overall nature of the 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No.3, June 2010 
segment [6,8]. If N is the number of observations, in single 
dimension, 






N=0 






. im 



kn 



where £=0,...,Af-l (1) 



Since the data set would be a bunch of time segments, the 
DFT would thus transform them from the time domain into 
frequency domain. It is easy and fast to compute. It preserves 
the distance between two objects. DFT ensures completeness 
of feature extraction [7]. 

B. Similarity Search 

Given a set of time series segments, there are two 
types of similarity searches: (i) sub segment matching finds all 
the data sub segments of a larger segment that are similar to 
the given smaller segment and (ii) whole matching finds those 
segments that are similar to one another. 

Two key aspects for achieving effectiveness and 
efficiency, when managing time series data are representation 
methods and similarity measures. Time series are essentially 
high dimensional data and dealing with raw data directly is 
very expensive in terms of processing and storage cost. Unlike 
canonical data types like ordinal or nominal variables, where 
the distance definition is straightforward, the distance between 
time series needs to be carefully defined in order to reflect the 
underlying similarity of such data. This is particularly 
desirable for similarity-based retrieval, classification and 
clustering of time series. 

Many similarity measures are efficient in accurately 
quantifying the similarity between any two distinct objects. 
Some of the efficient similarity measures to state can be Bray- 
Curtis distance, Canberra Distance, Cosine Distance, 
Correlation Distance and Chessboard Distance. However these 
measures were not able to capture the similarity perfectly 
between any two time variant segments. These methods were 
lacking in way that they compare only the corresponding 
points of the two data sets, but a time varying dataset is unique 
in its nature of being recorded at discrete and equal intervals 
of time and hence similarity measures exclusively for time 
variant data are to be considered. 

Past research, on the choice of distance/similarity 
function to obtain similarity measure between two segments 
that are time variant, reveal that it can be divided into two 
classes. The first and straightforward method is the Euclidean 
distance and its variants like Manhattan distance. The 
complexities of these measures are linear, they are easy to 
implement and parameter free. 

However, since the mapping between the points of 
two time series is fixed in the above two methods, these 
distance measures are very sensitive to noise and 
misalignments in time, and are unable to handle local time 
shifting i.e., similar segments that are out of phase, 

The second class includes the Dynamic Time 
Warping (DTW) [3]. Continuity is less important in DTW than 
in other pattern matching algorithms. DTW is an algorithm 
particularly suited to matching segments with missing 
information, provided there are long enough segments for 



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matching to occur. Optimal alignment [4,10] i.e., minimum 
distance time warp path is obtained by allowing assignment of 
multiple successive values of one time series to a single value 
of the other time series and therefore it can be calculated on 
time series of different lengths. 

Dynamic time warping is an algorithm for measuring 
similarity between two segments which may vary in time. 
DTW aligns two time series in the way some distance measure 
is minimized as shown in Fig. 1. It can efficiently handle noise 
and misalignments in time, and are able to handle local time 
shifting i.e., similar segments that are out of phase [9]. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No.3, June 2010 
Thus DTW gives a quantified measure of similarity 
between any two instances and smaller the value more is the 
similarity between them. So to impute any segment with 
missing data, it is required to compute DTW between the 
target segment and every other complete segment in the 
dataset. But since the dataset is very large it is cumbersome to 
calculate similarity measure with every other segment. Thus 
there arises a need for clustering wherein the raw data and 
their extracted features can be grouped as clusters each of 
which contains similar instances. 



\\ 



/ 


/ 


/ 


/ 

/ 
/ 
/ 

/ 


/ 

/ 


/ / 

/ 











Time 

Figure. 1 A warping between two time series 

In Fig. 1, each vertical line connects a point in one 
time series to its correspondingly similar point in the other 
time series. The lines actually have similar values on the y- 
axis but have been separated so the vertical lines between 
them can be viewed more easily. If both of the time series in 
Fig. 1 were identical, all of the lines would be straight vertical 
lines because no warping would be necessary to 'line up' the 
two time series. The warp path distance is a measure of the 
difference between the two time series after they have been 
warped together, which is measured by the sum of the 
distances between each pair of points connected by the vertical 
lines in Fig.l. Thus, two time series that are identical except 
for localized stretching of the time axis will have DTW 
distances of zero. Given two time series X, and Y, of lengths 
IXI and IYI; X= xi,x 2 ,...,x iv ..,x w and Y= yi,y 2v ..,y iv ..,yi y i, a 
warp path W is given by 

W= Wi,w 2 ,...,Wi,...,w k , where max(|X|,|Y|) <£< IXI+IYI) 
k is the length of the warp path and the k {h element of the warp 
path is W k =(i,j),w k+ i=(i k ,j k ) and i is an index from time series 
X, and j is an index from time series Y. The warp path must 
start at the beginning of each time series at wi = (1, 1) and 
finish at the end of both time series at w K = (IXI, IYI). This 
ensures that every index of both time series is used in the warp 
path. There is also a constraint on the warp path that forces i 
and j to be monotonically increasing in the warp path, which is 
why the lines representing the warp path in Fig. 1 do not 
overlap. Every index of each time series must be used. More 
formally, the optimal warp path is the warp path is the 
minimum-distance warp path, where the distance of a warp 
path W is 



Dist(w)=2J;-5 r Dist(iv w iv fcy ,) 



(2) 



Dist(W) is the distance, of warp path W, and Dist(w ki , 
w kj ) is the distance between the two data point indexes in the 
& th element of the warp path. 



C. Clustering 

Clustering is a division of data into groups of similar 
objects. Each group or a cluster consists of objects that are 
similar between themselves and dissimilar to objects of other 
groups. When the dataset under consideration is very large, 
manual handling of data proved to be inefficient. So clustering 
techniques are adopted to handle the data and thereby 
minimize the computations. 

The common clustering techniques include 
hierarchical clustering, partitioning clustering, grid based 
clustering and constraint based clustering. Some of the 
hierarchical clustering techniques are agglomerative clustering 
and divisive clustering, k-medoids, k-means, probabilistic and 
density based clustering techniques fall under the category of 
partitioned clustering. 

III. SIMILARITY BASED IMPUTATION METHOD 

The key objective of this paper is to fill out missing 
data that may be lost due to some inevitable reasons. Every 
data point adds to the machine learning or data mining process 
which implies that no data point can be ignored. So the need to 
fill missing data by some efficient method is needed that 
involves less computation and would fill in the missing 
observations more logically and accurately. This section 
discusses a new method to fill missing data called Similarity 
based Imputation Method (SIM) which involves filling by 
similarity search. The idea is to fill missing data by finding 
similar complete segments for the segments with missing data 
and impute them. 

SIM takes as input a large time variant dataset with 
missing segments. The imputation process consists of two 
phases. The first phase collects the significant parameters of 
the dataset and the second uses these values to impute. The 
steps in the first phase are: 

1 . Divide the entire dataset into segments of length /. 

2. Except for the segments with missing data, compute 
segment parameters for each of the segment in the 
dataset. 

3. Cluster the segments based on their segment 
parameters. 

The second phase that imputes the missing values consists of 
the following steps. The steps are applied on each segment 
with missing data. 



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1 . Compute segment parameters and classify the segment into 
an appropriate cluster. 

2. Divide each segment into sub segments. 

3. Extract DFT coefficients of all the sub segments. 

4. Using DFT coefficients find the most similar segment to 
the segment with missing data. Impute values from the 
complete segment into the segment with missing data 

A lot of segment parameters may be used based on 
which the segments can be clustered. A few possible 
parameters may be count of local maxima, count of local 
minima, position of global maxima, position of global minima, 
global maxima value and global minima value. For example 
for the sample dataset (223.9, 223.7, 223.6, 223.4, 223.3, 
223.3, 223.2, 223.1, 223.2, 223.4, 223.5, 223.6, 223.8, 223.7, 
223.8, 223.8, 223.8, 223.7, 223.7, 223.9, 223.9, 224.0, 224.1, 
224.2), the above said parameters are 7, 6, 24, 8, 224.2, 223.1, 
respectively. 



Phase 1 



Phase 2 



RAW 
DATA 



SEGMENT WITH 
MISSING DATA 



PARTITIONING 
CRITERIA 



i 



SEGMENT 
PARAMETERS 



DISCRETE 
SEGMENTS 



i 



CLASSIFICATION 
OF SEGMENT 



FEATURE 
EXTRACTION 



i 



FEATURE 
EXTRACTION 



SEGMENT 
PARAMETERS 



i 



PREPROC 
ESSING 



K-MEANS 
CLUSTERING 



i 



SIMILARITY 
MEASURE (DTW) 



CLUSTERS WITH NO 
MISSING DATA 



i 



COMPARISON WITH THE DFT 
OF OTHER SEGMENTS 



H 



IMPUTATION FROM THE 
MOST SIMILAR SEGMENT 



Figure. 2 Phase 1 and Phase 2 of imputation process 

To compute the DFT coefficients, each segment is 
divided into three sub segments. Let a and b be the positions 
from and to in the incomplete segment where the data are 



missing. The data from the first to (a-1) position forms the 
first sub segment; data from d v to b th position forms the 
second sub segment and the rest form the third sub segment of 
the segments. If (223.9, 223.7, 223.6, 223.4, 223.3, 223.2, -1, - 
1, -1, -1, -1, -1, 223.7, 223.8, 223.8, 223.8, 223.8, 223.7, 
223.7, 223.9) represents the segment and -1 represents the 
missing value, then a and b of all the segments would be 7 and 
12. The DFT coefficients of the upper and lower sub segments 
of the dataset are (591.283, -0.041,-0.091, 0.097, 0.312, 0.663) 
and (592.044, -0.252, -0.25, -0.178,0.039, 0.507) 

The identification of the similarity of the sub 
segments involves the computation of similarity measure 
DTW between every pair of sub segments. The candidate for 
the imputation is the segment that is most similar to upper and 
lower sub segments of the incomplete segment. The Fig. 2 
explains the steps pictorially. 

IV. EXPERIMENTAL EVALUATION 



The dataset considered for experimental evaluation of 
the method proposed in this paper is one of the three measures 
of earth's magnetic field variations which are measured along 
three axes as H- the horizontal intensity, Z- the vertical 
intensity and D the dip. The frequency of observation is one 
minute. The parameter that was used for evaluating the result 
is Relative Error Percentage (REP). If AV is the actual value 
and IV is the imputed value REP is given by 



REP = 



AV-IV 
AV 



(3) 



Table 1 Relative Error Percentage of various experimental 
setups 



%of 
Missing 
Points 


Region 

of 
Missing 
Points 


Number of Segments 


100 


200 


500 


1000 


5000 


10000 


20000 


5% 


Upper 


0.42 


0.17 


0.17 


0.17 


0.17 


0.17 


0.17 


Middle 


0.25 


0.128 


0.128 


0.128 


0.128 


0.128 


0.085 


Lower 


0.553 


0.043 


0.043 


0.043 


0.043 


0.043 


0.043 


4% 


Upper 


0.128 


0.06 


0.06 


0.06 


0.06 


0.06 


0.06 


Middle 


0.17 


0.128 


0.128 


0.128 


0.128 


0.128 


0.128 


Lower 


0.46 


0.045 


0.045 


0.045 


0.045 


0.045 


0.045 


3% 


Upper 


0.06 


0.06 


0.06 


0.06 


0.06 


0.06 


0.06 


Middle 


0.17 


0.128 


0.128 


0.128 


0.128 


0.128 


0.085 


Lower 


0.468 


0.043 


0.043 


0.043 


0.043 


0.043 


0.043 


2% 


Upper 


0.426 


0.06 


0.06 


0.06 


0.06 


0.06 


0.06 


Middle 


0.17 


0.128 


0.128 


0.128 


0.128 


0.128 


0.085 


Lower 


0.468 


0.043 


0.043 


0.043 


0.043 


0.043 


0.043 


1% 


Upper 


0.298 


0.213 


0.213 


0.213 


0.213 


0.213 


0.213 


Middle 


0.043 


0.043 


0.043 


0.043 


0.043 


0.043 


0.043 


Lower 


0.553 


0.043 


0.043 


0.043 


0.043 


0.043 


0.043 



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The segment lengths were fixed at 20. The segment 
parameters considered for the experiments were count local 
maxima, count of local minima, position of global maxima, 
position of global minima, value of global minima, value of 
global minima, number of local minima before global minima, 
number of local maxima after global minima, number of local 
minima before global maxima, number of local maxima after 
global maxima, number of local minima between global 
minima and global maxima and number of local maxima 
between global minima and global maxima. The various 
scenarios for which the experiment was conducted and the 
REP obtained are given in Table 1. The regions of missing 
data are indicators of the variations in the lengths of upper and 
lower sub segments of the segments. 

The following observations about the experiment and 
results are worth mentioning. 

• The repletion of the values in the table is a good indicator 
of the identification of same segment repeatedly 
irrespective of the increase in number of segments. 

• The best results were obtained as the numbers of segments 
were increased. 

• DTW similarity measure is an effective similarity measure 
for time variant data. 

• The increase in segment size also minimizes the error. 



(IJCSIS) International Journal of Computer Science and Information Security, 

Vol 8, No.3, June 2010 

Measures", Proceedings of the ninth ACM SIGKDD 

international conference on Knowledge discovery and data 

mining", Pages: 216 - 225,2003 
[4] Blaz Strle, Martin Mozina, Ivan Bratko:" Qualitative 

Approximation To Dynamic Time Warping Similarity 

Between Time Series Data", 23rd International Workshop 

on Qualitative Reasoning 2007 
[5] M.Sridharan, N.Gururajan and A.M.S.Ramasamy: "Fuzzy 

Clustering Analysis to study geomagnetic coastal effects" 
[6] M. D. Morse and J. M. Patel. "An efficient and accurate 

method for evaluating time series similarity ".In SIGMOD 

Conference, 2007. 
[7] I. Popivanov and R. J. Miller. "Similarity Search Over 

Time-Series Data Using Wavelets". In ICDE, 2002. 
[8] K.-P. Chan and A.-C. Fu." Efficient Time Series Matching 

by Wavelets". ICDE, pages 126-133, 1999 
[9] D. Berndt and J. Clifford." Using Dynamic Time Warping 

to Find Patterns in Time Series". In AAAI-94 Workshop 

on Knowledge Discovery in Databases, pages 359-370, 

1994. 
[10] B.-K. Yi, H. V. Jagadish, and C. Faloutsos. "Efficient 

Retrieval of Similar Time Segments Under Time 

Warping". In ICDE, pages 201-208, 1998. 

AUTHORS PROFILE 



V. CONCLUSION 

This proposed methodology can be applied to any 
dataset that suffers from potential loss of data due to 
instrumental and other unanticipated faults. It cuts out a 
solution for the problem of missing data by the strategy of 
Imputation, which involves finding the segment which is most 
similar to the one with missing data. Proficient feature 
extraction techniques are adopted to handle the voluminous 
data which represent continuous time variant data. This 
minimizes huge computations which need to be done when 
raw data is used for analysis. SIM performs better than other 
existing imputation and computational methods because the 
time complexity is minimized as the use of feature extraction 
and clustering techniques minimizes the search space to a 
single cluster. The relative percentage error is also very less 
which adds to the efficiency of the method. It can perform 
even better by improvising the clustering algorithms. 



F.Sagayaraj Francis is currently working as an Associate Professor in the 
Department of Computer Science and Engineering, Pondicherry Engineering 
College. He obtained his M.Tech. degree in Computer Science and 
Engineering in the year 1997 and Ph.D. degree in Computer Science and 
Engineering in the year 2008, both from Pondicherry University. His areas of 
research and interest are Data Management and Information Systems. 

B.Vishnupriya has finished her B.Tech in Computer Science and Engineering 
degree in the current year from Pondicherry University. 

Vinolin Deborah Delphin has completed her B.Tech in Computer Science and 
Engineering degree in the academic year from Pondicherry University. 

Saranya Kumari Potluri has done her B.Tech in Computer Science and 
Engineering degree in the current year from Pondicherry University. 



REFERENCES 

[1] Hui Ding, Goce Trajcevski, Peter Scheurmann, Xiaoyue 
Wang, Eamonn Keogh: "Querying And Mining Of Time 
Series Data: Experimental Comparison Of Representations 
And Distance Measures", Proceedings of VLDB 
Endowment, Vol. 1, Pages 1542-1552, 2008 

[2] Robert T.Olszewski: "Generalised Feature Extraction for 
Structural Pattern Recognition in Time Series Data" 

[3] Michail Vlachos, Marios Hadjielefttheriou, Dimitrios 
Gunopulos, Eamonn Keogh: "Indexing Multi-Dimensional 
Time-Series with Support for Multiple Distance 



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Efficient Node Search in P2P Using Distributed 

Spanning Tree 

jk & -b -bit 

P. Victer Paul T. Vengattaraman M.S.Saleem Basha P. Dhavachelvan R. Baskaran 
Department of Computer Science, Pondicherry University, Puducherry, India. 
# Department of Computer Science and Engineering, Anna University, Chennai, India. 

{victerpaul, vengat. mailbox, smartsaleeml979, dhavachelvan, baskaran. ramachandran}® gmail.com 



Abstract 

Peer to Peer (P2P) networks are the 
important part of the next cohort of Internet, so 
how to search the node in the P2P networks 
efficiently is the key problem of the perception of 
the P2P network. However, the node search 
process in unstructured P2P is not efficient 
because the same search message may go through 
a node multiple times. To ease the complex search 
and improve the search efficiency, we propose a 
mechanism using the interconnection structure 
called Distributed Spanning Tree (DST) which 
facilitates the P2P Network into a layered 
structure to improve the node search technique. 
The performance evaluations of simulation 
demonstrate that the proposed mechanism can 
improve the node search efficiency ofP2P systems. 

Keywords: Peer to Peer, Distributed Spanning 
Tree, Node Search, Ant colony optimization 

1. Introduction 

P2P networks are important part of the next 
generation of Internet, so how to search the 
resources in the P2P networks efficiently is the 
key problem of the realization of the P2P network 
[1]. The advantages of the unstructured P2P 
networks are that they have lower maintenance 
overhead and can better adapt to node 
heterogeneity as well as network dynamics. 
However, the node search process in unstructured 
systems is not as efficient because the same search 
message may go through a node multiple times [2]. 
For communication in network, operation for 
search of a particular node is performed often 
which consumes more message passes even the 
node identification factor like IP address are 
known. Since routing information stored by each 



node is limited that makes the search operation 
complex in large scale networks. To facilitate the 
complex search and improve the search efficiency, 
we propose a novel approach of node search using 
DST technique. This reduces the message passes 
required to identify a node and prevent nodes 
from receiving duplicate search messages and 
retains the low maintenance overhead for the 
unstructured system. 

From these perspectives, in this paper, it is 
aimed at developing an optimized node search 
system using Distributed Spanning Tree (DST), 
which will reduce the number of message passes 
required in deterministic environment. Various 
requirements to reduce the message pass can be 
achieved by formulating DST in the network. 
Performance of this improved mechanism is 
simulated and analyzed using a theme specific 
environment for Node search. The paper is 
organized as follows: Section 2 defines the 
Interconnection Structure used and the respective 
constrains. Section 3 explains about Formulation 
of DST in P2P network. Section 4 discusses about 
proposed mechanisms for Node search in P2P 
network. Section 5 provides statistical Analysis 
over result obtained in the Simulation and Section 
6 concludes the proposed work with its merit. 

2. Interconnection Structure Used 

Distributed Spanning Tree (DST) [9, 10] is the 
interconnection structure we follow to reduce the 
number of message passes required for node 
search. DST organizes P2P network into a 
hierarchy of groups of nodes. The nodes are put 
together in groups and groups are gathered in 
higher level recursively. This organization is built 
on top of routing tables allows the instantaneous 



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creation of spanning trees rooted by many nodes 
and keeps the load balanced between the nodes [3]. 
The DST is an overlay structure designed to be 
scalable [4]. The DST is a tree without 
bottlenecks which automatically balances the load 
between its nodes. From [3] and [4], it is possible 
to achieve economic traffic optimization in Peer 
Network using DST interconnection structure. So 
we virtually convert the peer network into 
distributed spanning trees and each tree should 
have its root node we call it as Head Node (HN) 
and others are Leaf Node (LN). Every LN will 
hold the details of its own HN. Likewise every 
HN will hold the complete details regarding its 
LNs and all other HNs in the network. The details 
stored in HNs and LNs in the DST is used to 
enhance the node search operation efficiently with 
minimum message pass. During the formulation 
of DST in Peer network LNs and HNs are chosen 
randomly and dynamically with some requirement 
criteria which improve the Fault Tolerance of the 
system. 

To enhance the efficiency of DST in Node 
Search, we focus on a kind of randomized search 
heuristics, namely Ant Colony Optimization 
(ACO). ACO [5, 6, 7, 8], is a powerful heuristic 
approach to solve combinatorial optimization 
problems such as the TSP, Routing in 
telecommunication networks. So applying ACO 
approach can enhance the effective routing of 
message (at low cost) in the network which in-tern 
reduces the number of message pass required. 

3. Formulation of DST 

In a large P2P network, formation of DST is 
complex. We elucidate the DST formation in the 
P2P network using five procedures. 

Firstly initialize _D ST is a procedure which 
initializes DST by creating Head Node (HN) in 
Peer network based on some test criteria(s). The 
criteria(s) to be checked can be user approval, 
traffic on a particular region, etc., and the 
procedure creates an array on each HN to hold its 
LN details. If the criteria(s) fail a variable on the 
node is created. Each HN is provided with unique 
Priority Number (PN) to provide write priority 
among the HNs. initialize _D ST is also a 



procedure to set its HN id as their own id and then 
it calls the procedure probe _DST{). 

The procedure probe_DST(), which is called 
by every HN creates probe message and set 'id' 
field of message as its own id and flood the 
message to all peers it is connected. 

On receiving a message every peer execute the 
procedure msg_recieve_DST(msg) where 'msg' is 
the received message. During DDST formation it 
should be possible to get any one of the two types 
of messages, the probe message or reply message: 

If there is a probe message, any one of the 
following would be occurred: 

Case-1: The message is received by a HN: It is 
just discarded. 

Case-2a: The message is received by a LN which 
is not under any HN: LN stores the Head variable 
as the id which it read from pmsg. Then call the 
procedures probe _reply_DST(N(id)) and 

probe Jorword_DST(pmsg). 

Case-2b: The message is received by a LN, which 
is under any HN: It is just discarded. 

If there is a reply message, any one of the 
following would be occurred: 

Case-1: The message is received by a LN: It just 
forwards it to the node bearing the id 'id'. 

Case-2: If the message is received by a HN: It 
reads 'dest' from 'rmsg', if 'dest' equals N(id) it 
shows required HN is reached. It read 'id' from 
'rmsg' and add it to its array, otherwise it is 
forwarded to N(id). 

Procedure probe_reply_DST(N(id)) is called 
by LN to reply to its HN. The LN creates a reply 
message. The 'id' and 'dest' fields of the reply 
message is set to be, the 'id' of the LN and the 
'id' of the HN respectively. After the reply 
message sent to HN, the LN calls the Procedure 
probe Jorword_DST(pmsg) to flood the probe 
message to all the peers except the peer from 
where it was received. 

After the completion of these five procedures 
the Peer Network will be in required DST 
structure. 



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Definition 1. Let n(DST msgpass ) be the Number of 

Message Pass Required to form DST in the Peer 
Network and it can be defined as, 



n{DST msgp J = {{LIP)*P*M) + {(LIPrN) (1) 



In equation (1), 'UP 9 gives the number of 
DST formed in the network which is also equal to 
n(HN). So equation (1) can be rewritten as, 



n ( DST ms 8P ass) : 



(L*M) + (n(HN)*N) 



(2) 



where, 

• 'N' be the number of message pass between 
one HN and another HN 

• 'M' be the number of message pass between 
HN and LN 

• 'L' be the number of Peers in the Network 

• T' be the number of LN under HN (consider 
equal number of LN for all HN) 

• '(L/P)' be the number of DST in the Peer 
Network (or equals n(HN)) 

• It can be interpretable that 1 < M < N < L and 
\<P<L 

In words, Total number of Message Pass 
required to form DST in the Peer Network 
n(DST msgpass ) is equal to sum of products between 

number of Peers in the Network and the number 
of message pass between HN and LN and between 
the number of HNs in the Network and the 
number of message pass between one HN and 
another HN in the Network. 

4. Proposed Efficient Node Search in Large 
scale P2P network using DST 

P2P networks are an important part of the next 
generation of Internet, so how to search the node 
in the P2P networks efficiently is the key problem 
of the realization of the P2P network. While 
studying the search technology of different 
mechanisms, this paper proposes a method to 
improve the node search in a large scale P2P 
network using the systematic DST approach. We 
propose a Node search algorithm in fig. 1 which 



consists of five procedures Request(), 
toAUHeads(), Found(), Reply() and Receive(). 

Procedure Request() is invoked by any node in 
network which want to identify a node for 
communication. This procedure has two 
arguments the source node v and destination node 
d and creates doFind message with source and 
destination details and sent to HN of source. 

Procedure toallHeads() is invoked by node v 
(should be a HN) when it receives Request 
message to propagate the message to all other 
HNs in the network using its HN_ARR array. 

Procedure Found() is invoked by the HN 
which hold the requested destination node d. This 
procedure create the foundDest message and set 
fields 'Dest' and 'Head2' as id of destination node 
d and id of current HN. 

Procedure Reply() is called by the Requester 
HN to forward the foundDest message to the 
source node v. 

Procedure Receive() is invoked the node v, 
when it receives any message. The received 
message should be any of three variants; doFind 
message, toallHeads message and foundDest 
message. 

If the received message by node v is a doFind 
message, any one of the following would be 
occurred: 

Case-1: If the node v is HN and 'Head' field of 
the message is 'id' of the node v, then it is 
procedure toAUHeads() is invoked. 

Case-2: If case-1 fails, then the node v forwards 
the message to the node with id in the 'Head' field 
of the message. 

If the received message by node v is a toallHeads 
message, any one of the following would be 
occurred: 

Case-1: If the node v is HN and 'Head2' field of 
the message is 'id' of the node v, then node v 
retrieve the 'd' field of message and matches it 
with LN entry in its LN_ARR array. There may 
two possible ways, 



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Case-la: If 'd' field in the message matches with 
LN entry of array LN_ARR in v which means that 
the search node d is under the current HN v. Then 
node v invoke Found() procedure to intimate the 
requester HN. 



Case-1: If the node v is HN and 'Headl' field of 
the message is 'id' of the node v, then it is 
procedure Reply() is invoked. 

Case-2: If the node v is LN and V field of the 



Requested) 


Receive(msg,v) 


Step 1 : create doFind message, dmsg 


Step 1: check if msg = dmsg goto step 2, if 


Set Sour field as v.ID 


msg = hmsg goto step 5 


Set Dest field as d.ID 


if msg -fmsg goto step 8 


Set Head field as v.Head 


Step 2: check if v = HN and v.ID = 


Step 2: send(dmsg,v.Head) 


dmsg.Head, goto step 3 else step 4 




Step 3: call toAUHeads(\j)msg) 

Step 4: forward(pm,sg,pmsg.Head) 


Found(s,d,HNl,HN2) 


Step 1: create foundD est message, fins g 


Step 5: check if v = HN and v.ID = 


Set Sour field as dmsg.Sour 


hmsg. Headl, goto step 6 else step 10 


Set Dest field as dmsg.Dest 


Step 6: VLN such that LN E v.LNs, repeat 


Set Headl field as HN1 


step 7 


Set Head2 field as v.ID 


Step 7: if hmsg A = LN.ID, goto step 8 else 


Step 2: send (ftnsg 9 HN1) 


step 9 




Step 8: call Found(/imsg.s,LN.ID, 
hmsg.Headl, v.ID) 


toAHHeads(v,dmsg) 


Step 1: VHN such that HN £ v.HNs 


Step 9: delete pmsg 


repeat step 2 and step 3 


Step 10: forwaid(hmsg,hmsg.llead2) 


Step 2: create toallHeads message, hmsg 


Step 11: check if v = HN and v.ID = 


Set Sour field as dmsg.Sour 


fmsg.Headl, goto step 12 


Set Dest field as dmsg.Dest 


if v = LN and v.ID = fmsg.s, goto 


Set Headl field as v.ID 


step 13 else step 14 


Set Head2 field as HN.ID 


Step 12: call Reply (fmsg) 


Step 3: send (hmsg, UN. ID) 


Step 13: // communication starts 
Step 14: forward(/msg,fmsg.s) 




Reply(fmsg) 




Step 1: send(fmsg,fmsg.Sour) 





Figure 1 . Proposed Node Search algorithm using DST structure 



Case- lb: If no matches found in LN_ARR array 
of HN v, then it discards the message. 

Case-2: If case-1 fails, then the node v forwards 
the message to the node with id in the 'Head2' 
field of the message. 

If the received message by node visa foundDest 
message, any one of the following would be 
occurred: 



message is 'id' of the node v, then it shows that 
the destination d is found. The node v retrieves 
'Headl', 'Head2' fields of message which it uses 
for communication with destination d through 
destination HN Headl. 

Case-3: If both case-1 and case-2 fails, then the 
node v forwards the message to the node with id 
in the 's' field of the message. 

Thus the Proposed Node search algorithm uses 
the details stored by every HNs regarding its LNs. 



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This approach makes the node search operation in 
a large scale P2P network more efficient and 
economic with very minimum number of message 
passes and reduced routing table entry in every 
node in the network. 

Definition 2. Let Pi be a peer in Peer Network. 
Then the number of routing table entries required 
for node Pi in the DST Peer network to route any 
message it receives is n(P t (RT _ entry)) and 
given as, 



n(P t (RT _ entry)) = 
n(HN) + {n(HN { (LN_ARR))Kj r(P.)} 



(3) 



where, 

• ' n(P t (RT _ entry)) ' is the number of routing 

table entries required for node Pi to route any 
message it receives. 

• ' n(HN) ' is the number of HNs in the 
Network 

• ' n(HN \(LN_ARR))' is the number LNs in 
theLN_ARRofHNofPi. 

• ' r(Pj) ' is the number of nodes adjacent to Pi. 

Thus the number of routing table entries 
required for node Pi to route any message it 
receives n(P t (RT _ entry)) is sum of number of 

HNs in the network and combination (without 
duplicate) of number of LNs in the LN_ARR of 
HN of Pi and number of nodes adjacent to node Pi. 

5. Simulation and Analysis 

This section describes the simulation results 
obtained during the investigation phases. We used 



OMNeT++, is an object-oriented modular discrete 
event network simulator. A Peer Network of 100 
peers (compl, comp2, comp3...compl00) 
interconnected randomly and spread in some 
distant geographical location to validate the 
proposed technique. The proposed Node search 
technique is implemented in the simulated Peer 
network and performed various fine grained 
analyses. . It is assumed that the medium have 
propagation delay of 10 ms. 

In our simulation setup of Peer Network with 
one hundred systems, n(DST msgpass ) is nearly 

equals 391 messages which take nearly 5.83 
seconds for the formation of complete DST (i.e.) 
to organize P2P network into a hierarchy of 
groups of nodes. 

5.1 DST Routing Scheme 

In this scheme, to route message from source 
to destination we used Static Routing technique. 
From the simulation we gathered different criteria 
measures and tabulated in Table I which show that 
the node search operations performed efficiently 
and economically in DST Peer Network than that 
of Typical Peer Network. Analyses of large P2P 
networks (with more than 100 peers) are 
cautiously derived from the simulated network. 
On simulation proceeds, it is observed that for the 
time period of 10 seconds number of node search 
operations performed in Typical Peer network and 
DST Peer network are 19 and 32 respectively. 
Thus the efficiency of node search operation in 
Peer network can be improved nearly 68.9% by 
using DST technique. 



Table I. Comparison of various criteria measures between Typical and DST Peer Network 



S.No 


No. 

of 

Peers 


No. 

of 

HNs 


Average 

Routing 

table 

entries 

(approx.) 


Typical Peer Network 


DST Peer Network 


Efficiency 


No. of 

nodes 

involved 


No. of 
node 
search 
operations 


Time 
taken 

(sec) 


No. of 

nodes 

involved 


No. of 
node 
search 
operations 


Time 
taken 

(sec) 


1 


10 2 


6 


24 


9 


19 


10 


12 


32 


10 


68.9% 


2 


10 3 


24 


67 


12 


26 


5 


21 


48 


5 


84.6% 


3 


10 4 


130 


202 


19 


51 


2 


36 


92 


2 


80.5% 


4 


10 5 


350 


601 


31 


94 


1 


61 


178 


1 


89.3% 



237 



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Table II. Comparison of various criteria measures between DST and ACO optimized DST Peer Network 



S.No 


No. 

of 

Peers 


No. 

of 

HNs 


Average 
Routing 
table entries 
(approx.) 


DST Peer Network 


ACO DST Peer Network 


Efficiency 


No. 

of 

nodes 

invol 

ved 


No. of 
node 
search 
operations 


Time 
taken 

(sec) 


No. of 

nodes 

involved 


No. of 
node 
search 
operations 


Time 
taken 

(sec) 


1 


10 2 


6 


24 + 6 


12 


32 


10 


16 


44 


10 


37.5% 


2 


10 3 


24 


67 + 23 


21 


48 


5 


29 


68 


5 


41.6% 


3 


10 4 


130 


202+121 


36 


92 


2 


48 


131 


2 


42.3% 


4 


10 5 


350 


601 + 321 


61 


178 


1 


72 


214 


1 


20.2% 



5.2 DST with Ant Colony Optimized Routing 
Scheme 

To route message from source to destination 
Ant Colony Optimized routing technique is 
followed which improve the message pass 
efficiency of DST because of dynamically 
identified optimal route between every HN and 
LN Peers and alternate optimal route between 
nodes. Table 2 shows the criteria measure 
between DST Peer Network and ACO optimized 
DST Peer Network and from the result analysis, it 
is evident that the efficiency of node search 
technique is improved by ACO optimization. On 
simulation proceeds, it is observed that for the 
time period of 10 seconds number of node search 
operations performed in Typical Peer network and 
DST Peer network are 19 and 32 respectively. 
Thus the efficiency of node search operation in 
Peer network can be improved nearly 37.5% by 
using Ant Colony Optimized DST technique. 
Analyses of large P2P networks (with more than 
100 peers) are cautiously derived from the 
simulated network. 

From the Table I and II, the following observation 
can be derived. 

Observation 1. 

Average number of routing table entries required 
by each peer in DST is more than that of ACO 
optimized DST. 

Proof. In DST network, every peer maintain 
single optimal path for a destination whereas in 
ACO technique find more than one optimal path 



between a single source and destination to 
increase the reliability on channel failures. In 
ACO, usually alternate optimal path is maintained 
between each Peer and all HNs. the number 
routing table entries required by any peer 
n(P t (RT _ entry)) given in equation (3) can be 
expressed as, for DST, 



n(P i (RT_entr$)= 
niHty + inittN^LN _ARR))u t(P { )} 

For ACO optimized DST, 



(4) 



n(P t (RT _ entry)) = 
{2 * n(HN)}+ { nCHN; (LN _ ARR)) u r^ )} (5) 

where, 

• ' n(HN) ' is the number of HNs in the 
network 

• i n(RN i (LN_ARR))' is the number LNs in 
theLN_ARRofHNofPi. 

• ' r(Pj) ' is the number of nodes adjacent to Pi. 

From equation (4) and (5), we can conclude 
that the number routing table entries required by 
any peer in ACO optimized DST will be more 
than that of DST. 

Observation 2. 

With increase in the size of the P2P network, 

efficiency of DST technique in node search also 

increases. 



238 



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4 

I 



I Typical P2P DDSTP2P 



lll.lll 



comp02 compll compl6 comp22 comp26 comp34 comp35 comp44 comp61 



Figure 2. Number node search operations performed by nodes in Typical and DDST P2P 




comp02 compll compl6 cornp22 comp26 cornp34 cornp35 cornp44 cornp61 



Figure 3 Number node search operations performed by nodes in DDST and ACO DDST P2P 



Proof. Number of HNs in the network increases 
with number of nodes. Consequently the increase 
in the node search operations is distributed among 
the HNs. So, large number of node search 
operations is performed effectively without any 
bottleneck which increases the overall efficiency 
of the node search technique using DST. Thus 
from Table 1, we can conclude that with increase 
in the size of the P2P network, efficiency of DST 
technique in node search also increases. 

Figure 2 and 3 shows the comparison graph 
between the number of node search operations 
performed by nodes in P2P network (only few 
among the total nodes involved in the search 
operation). From which we can conclude that the 
efficiency of node search can be improved using 
DST technique and ACO technique. 

From the simulation analysis, the proposed 
node search technique optimizes the traffic in 



efficient manner by reducing the message pass 
required for search operation in P2P networks. 

6. Conclusion 

One of the important issues with large 
P2P networks is that a node search message may 
go through the same node multiple times, which 
causes inefficiency of the node search process. 
We proposed a mechanism to deal with the 
problem by using interconnection structure DST 
which will reduce the number of message passes 
required for node search in the Peer Network. An 
enhanced algorithm ACO is used to optimize DST, 
which offered better performance, is also 
presented. From the simulation analysis, it is 
shown that in the Peer Network with DST, high 
performance node search can be achieved than 
ordinary Peer Network. 



239 



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ISSN 1947-5500 



Reference 

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International Journal Computer Science and Information Security, now at its sixth edition, is the premier 
scholarly venue in the areas of computer science and security issues. IJCSIS 2010 will provide a high 
profile, leading edge platform for researchers and engineers alike to publish state-of-the-art research in the 
respective fields of information technology and communication security. The journal will feature a diverse 
mixture of publication articles including core and applied computer science related topics. 

Authors are solicited to contribute to the special issue by submitting articles that illustrate research results, 
projects, surveying works and industrial experiences that describe significant advances in the following 
areas, but are not limited to. Submissions may span a broad range of topics, e.g.: 

Track A: Security 

Access control, Anonymity, Audit and audit reduction & Authentication and authorization, Applied 
cryptography, Cryptanalysis, Digital Signatures, Biometric security, Boundary control devices, 
Certification and accreditation, Cross-layer design for security, Security & Network Management, Data and 
system integrity, Database security, Defensive information warfare, Denial of service protection, Intrusion 
Detection, Anti-malware, Distributed systems security, Electronic commerce, E-mail security, Spam, 
Phishing, E-mail fraud, Virus, worms, Trojan Protection, Grid security, Information hiding and 
watermarking & Information survivability, Insider threat protection, Integrity 

Intellectual property protection, Internet/Intranet Security, Key management and key recovery, Language- 
based security, Mobile and wireless security, Mobile, Ad Hoc and Sensor Network Security, Monitoring 
and surveillance, Multimedia security , Operating system security, Peer-to-peer security, Performance 
Evaluations of Protocols & Security Application, Privacy and data protection, Product evaluation criteria 
and compliance, Risk evaluation and security certification, Risk/vulnerability assessment, Security & 
Network Management, Security Models & protocols, Security threats & countermeasures (DDoS, MiM, 
Session Hijacking, Replay attack etc,), Trusted computing, Ubiquitous Computing Security, Virtualization 
security, VoIP security, Web 2.0 security, Submission Procedures, Active Defense Systems, Adaptive 
Defense Systems, Benchmark, Analysis and Evaluation of Security Systems, Distributed Access Control 
and Trust Management, Distributed Attack Systems and Mechanisms, Distributed Intrusion 
Detection/Prevention Systems, Denial-of-Service Attacks and Countermeasures, High Performance 
Security Systems, Identity Management and Authentication, Implementation, Deployment and 
Management of Security Systems, Intelligent Defense Systems, Internet and Network Forensics, Large- 
scale Attacks and Defense, RFID Security and Privacy, Security Architectures in Distributed Network 
Systems, Security for Critical Infrastructures, Security for P2P systems and Grid Systems, Security in E- 
Commerce, Security and Privacy in Wireless Networks, Secure Mobile Agents and Mobile Code, Security 
Protocols, Security Simulation and Tools, Security Theory and Tools, Standards and Assurance Methods, 
Trusted Computing, Viruses, Worms, and Other Malicious Code, World Wide Web Security, Novel and 
emerging secure architecture, Study of attack strategies, attack modeling, Case studies and analysis of 
actual attacks, Continuity of Operations during an attack, Key management, Trust management, Intrusion 
detection techniques, Intrusion response, alarm management, and correlation analysis, Study of tradeoffs 
between security and system performance, Intrusion tolerance systems, Secure protocols, Security in 
wireless networks (e.g. mesh networks, sensor networks, etc.), Cryptography and Secure Communications, 
Computer Forensics, Recovery and Healing, Security Visualization, Formal Methods in Security, Principles 
for Designing a Secure Computing System, Autonomic Security, Internet Security, Security in Health Care 
Systems, Security Solutions Using Reconfigurable Computing, Adaptive and Intelligent Defense Systems, 
Authentication and Access control, Denial of service attacks and countermeasures, Identity, Route and 



Location Anonymity schemes, Intrusion detection and prevention techniques, Cryptography, encryption 
algorithms and Key management schemes, Secure routing schemes, Secure neighbor discovery and 
localization, Trust establishment and maintenance, Confidentiality and data integrity, Security architectures, 
deployments and solutions, Emerging threats to cloud-based services, Security model for new services, 
Cloud-aware web service security, Information hiding in Cloud Computing, Securing distributed data 
storage in cloud, Security, privacy and trust in mobile computing systems and applications, Middleware 
security & Security features: middleware software is an asset on 

its own and has to be protected, interaction between security- specific and other middleware features, e.g., 
context-awareness, Middleware-level security monitoring and measurement: metrics and mechanisms 
for quantification and evaluation of security enforced by the middleware, Security co-design: trade-off and 
co-design between application-based and middleware-based security, Policy-based management: 
innovative support for policy-based definition and enforcement of security concerns, Identification and 
authentication mechanisms: Means to capture application specific constraints in defining and enforcing 
access control rules, Middleware-oriented security patterns: identification of patterns for sound, reusable 
security, Security in aspect-based middleware: mechanisms for isolating and enforcing security aspects, 
Security in agent-based platforms: protection for mobile code and platforms, Smart Devices: Biometrics, 
National ID cards, Embedded Systems Security and TPMs, RFID Systems Security, Smart Card Security, 
Pervasive Systems: Digital Rights Management (DRM) in pervasive environments, Intrusion Detection and 
Information Filtering, Localization Systems Security (Tracking of People and Goods), Mobile Commerce 
Security, Privacy Enhancing Technologies, Security Protocols (for Identification and Authentication, 
Confidentiality and Privacy, and Integrity), Ubiquitous Networks: Ad Hoc Networks Security, Delay- 
Tolerant Network Security, Domestic Network Security, Peer-to-Peer Networks Security, Security Issues 
in Mobile and Ubiquitous Networks, Security of GSM/GPRS/UMTS Systems, Sensor Networks Security, 
Vehicular Network Security, Wireless Communication Security: Bluetooth, NFC, WiFi, WiMAX, 
WiMedia, others 



This Track will emphasize the design, implementation, management and applications of computer 
communications, networks and services. Topics of mostly theoretical nature are also welcome, provided 
there is clear practical potential in applying the results of such work. 

Track B: Computer Science 

Broadband wireless technologies: LTE, WiMAX, WiRAN, HSDPA, HSUPA, Resource allocation and 
interference management, Quality of service and scheduling methods, Capacity planning and dimensioning, 
Cross-layer design and Physical layer based issue, Interworking architecture and interoperability, Relay 
assisted and cooperative communications, Location and provisioning and mobility management, Call 
admission and flow/congestion control, Performance optimization, Channel capacity modeling and analysis, 
Middleware Issues: Event-based, publish/subscribe, and message-oriented middleware, Reconfigurable, 
adaptable, and reflective middleware approaches, Middleware solutions for reliability, fault tolerance, and 
quality-of- service, Scalability of middleware, Context-aware middleware, Autonomic and self-managing 
middleware, Evaluation techniques for middleware solutions, Formal methods and tools for designing, 
verifying, and evaluating, middleware, Software engineering techniques for middleware, Service oriented 
middleware, Agent-based middleware, Security middleware, Network Applications: Network-based 
automation, Cloud applications, Ubiquitous and pervasive applications, Collaborative applications, RFID 
and sensor network applications, Mobile applications, Smart home applications, Infrastructure monitoring 
and control applications, Remote health monitoring, GPS and location-based applications, Networked 
vehicles applications, Alert applications, Embeded Computer System, Advanced Control Systems, and 
Intelligent Control : Advanced control and measurement, computer and microprocessor-based control, 
signal processing, estimation and identification techniques, application specific IC's, nonlinear and 
adaptive control, optimal and robot control, intelligent control, evolutionary computing, and intelligent 
systems, instrumentation subject to critical conditions, automotive, marine and aero-space control and all 
other control applications, Intelligent Control System, Wiring/Wireless Sensor, Signal Control System. 
Sensors, Actuators and Systems Integration : Intelligent sensors and actuators, multisensor fusion, sensor 
array and multi-channel processing, micro/nano technology, microsensors and micro actuators, 
instrumentation electronics, MEMS and system integration, wireless sensor, Network Sensor, Hybrid 



Sensor, Distributed Sensor Networks. Signal and Image Processing : Digital signal processing theory, 
methods, DSP implementation, speech processing, image and multidimensional signal processing, Image 
analysis and processing, Image and Multimedia applications, Real-time multimedia signal processing, 
Computer vision, Emerging signal processing areas, Remote Sensing, Signal processing in education. 
Industrial Informatics: Industrial applications of neural networks, fuzzy algorithms, Neuro-Fuzzy 
application, biolnformatics, real-time computer control, real-time information systems, human-machine 
interfaces, CAD/CAM/CAT/CIM, virtual reality, industrial communications, flexible manufacturing 
systems, industrial automated process, Data Storage Management, Harddisk control, Supply Chain 
Management, Logistics applications, Power plant automation, Drives automation. Information Technology, 
Management of Information System : Management information systems, Information Management, 
Nursing information management, Information System, Information Technology and their application, Data 
retrieval, Data Base Management, Decision analysis methods, Information processing, Operations research, 
E-Business, E-Commerce, E-Government, Computer Business, Security and risk management, Medical 
imaging, Biotechnology, Bio-Medicine, Computer-based information systems in health care, Changing 
Access to Patient Information, Healthcare Management Information Technology. 
Communication/Computer Network, Transportation Application : On-board diagnostics, Active safety 
systems, Communication systems, Wireless technology, Communication application, Navigation and 
Guidance, Vision-based applications, Speech interface, Sensor fusion, Networking theory and technologies, 
Transportation information, Autonomous vehicle, Vehicle application of affective computing, Advance 
Computing technology and their application : Broadband and intelligent networks, Data Mining, Data 
fusion, Computational intelligence, Information and data security, Information indexing and retrieval, 
Information processing, Information systems and applications, Internet applications and performances, 
Knowledge based systems, Knowledge management, Software Engineering, Decision making, Mobile 
networks and services, Network management and services, Neural Network, Fuzzy logics, Neuro-Fuzzy, 
Expert approaches, Innovation Technology and Management : Innovation and product development, 
Emerging advances in business and its applications, Creativity in Internet management and retailing, B2B 
and B2C management, Electronic transceiver device for Retail Marketing Industries, Facilities planning 
and management, Innovative pervasive computing applications, Programming paradigms for pervasive 
systems, Software evolution and maintenance in pervasive systems, Middleware services and agent 
technologies, Adaptive, autonomic and context-aware computing, Mobile/Wireless computing systems and 
services in pervasive computing, Energy-efficient and green pervasive computing, Communication 
architectures for pervasive computing, Ad hoc networks for pervasive communications, Pervasive 
opportunistic communications and applications, Enabling technologies for pervasive systems (e.g., wireless 
BAN, PAN), Positioning and tracking technologies, Sensors and RFID in pervasive systems, Multimodal 
sensing and context for pervasive applications, Pervasive sensing, perception and semantic interpretation, 
Smart devices and intelligent environments, Trust, security and privacy issues in pervasive systems, User 
interfaces and interaction models, Virtual immersive communications, Wearable computers, Standards and 
interfaces for pervasive computing environments, Social and economic models for pervasive systems, 
Active and Programmable Networks, Ad Hoc & Sensor Network, Congestion and/or Flow Control, Content 
Distribution, Grid Networking, High-speed Network Architectures, Internet Services and Applications, 
Optical Networks, Mobile and Wireless Networks, Network Modeling and Simulation, Multicast, 
Multimedia Communications, Network Control and Management, Network Protocols, Network 
Performance, Network Measurement, Peer to Peer and Overlay Networks, Quality of Service and Quality 
of Experience, Ubiquitous Networks, Crosscutting Themes - Internet Technologies, Infrastructure, 
Services and Applications; Open Source Tools, Open Models and Architectures; Security, Privacy and 
Trust; Navigation Systems, Location Based Services; Social Networks and Online Communities; ICT 
Convergence, Digital Economy and Digital Divide, Neural Networks, Pattern Recognition, Computer 
Vision, Advanced Computing Architectures and New Programming Models, Visualization and Virtual 
Reality as Applied to Computational Science, Computer Architecture and Embedded Systems, Technology 
in Education, Theoretical Computer Science, Computing Ethics, Computing Practices & Applications 



Authors are invited to submit papers through e-mail ijcsiseditor @ gmail.com . Submissions must be original 
and should not have been published previously or be under consideration for publication while being 
evaluated by IJCSIS. Before submission authors should carefully read over the journal's Author Guidelines, 
which are located at http://sites.google.com/site/ijcsis/authors-notes . 






© IJCSIS PUBLICATION 2010 
ISSN 1947 5500