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Title of Thesis

Ants inspired Traffic classification in Mobile Ad Hoc Networks

Author(s)

Babar Nazir

Institute/University/Department Details
Department of Electrical Engineering / University of Engineering and Technology, Peshawar
Session
2011
Subject
Electrical Engineering
Number of Pages
187
Keywords (Extracted from title, table of contents and abstract of thesis)
Bandwidths, Mobile, Inspired, Routing, Communication, Independent, Networks, Intelligence, Bandwidths, Environment, Ants, Conventional, Classification, Traffic

Abstract
Mobile ad hoc network is new paradigm in communication networks.Unlike wired or binternet, these are wireless networks consisting of mobile nodes which are rapidly deployable and self organizing in nature. Communication between network nodes takes place through scarce wireless channels.Each node is capable of relaying the traffic of other nodes, so called multi hop network.
The rising popularity and commercial usage of MANETs demands support for Quality of Service in such networks to work effectively.Supporting QoS in such ad hoc environments has been broadly accepted as a tricky problem due to particular uniqueness of these networks such as dynamics of the environment, node mobility, and improbability of availability of resources.
Active research is underway in the area of providing quality of service in mobile ad hoc networks but work is still considered in its nascent stage, which can broadly be categorized in two main streams.One deals with proposing extensions in already proposed best effort routing protocols, while other is dedicated towards developing purpose built QoS aware routing protocols based on conventional routing approaches. Both of these strategies are unable to offer a mature solution for dynamics of MANET environment.
Recent research in swarm intelligence has revealed matching properties between the routing requirements of communication networks and certain tasks that exist in social insect colonies. Research community has proposed promising solutions for routing in dynamic ad hoc networks on the basis of these swarm intelligence principles.
In this thesis, in the first stage, simulation has been carried out for performance evaluation of two leading MANET routing protocols to emphasize the ineffectiveness of conventional routing techniques. In the second stage, a novel QoS provisioning scheme called Ants Inspired Traffic Classification in Mobile Ad Hoc Networks has been developed which is based on Swarm intelligence obtained through stigmergy process found in nature like ant colony.Most of the conventional routing approaches heavily rely on feedback from other layers while this approach is layer independent in nature.
Service classes are made available through the proposed solution in order to serve according to priority of the traffic. The solution uses an adaptive approach giving alternate QoS supporting paths, which are instantly made available in case of degraded performance that often happens in dynamic environment of MANET. Redundant routes often appear in MANET’s dynamic environment and remain present in routing tables thus increasing overheads.The proposed solution addresses this issue through evaporation property of Pheromone in Swarm Intelligence.
In our case, the most important parameter of QoS is considered in terms of Bandwidth. Our SI based protocol defines traffic paths in the network of various classes belonging to different range of bandwidths.

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1,574 KB
S. No. Chapter Title of the Chapters Page Size (KB)
1 0 CONTENTS

 

vii
19 KB
2

1

INTRODUCTION

1.1 General
1.2 Problem Statement
1.3 Objectives and Scope
1.4 Original Contributions
1.5 Organization of Thesis

1
30 KB
3 2 TECHNICAL BACKGROUND

2.1 Mobile Ad Hoc Networks
2.2 Mobile Ad Hoc Networks Protocol Layer Stack
2.3 Mobile Ad Hoc Networks Applications
2.4 MANET Characteristics
2.5 MANET Challenges
2.6 Quality of Service
2.7 Conclusion

7
170 KB
4 3 ROUTING RELATED WORK

3.1 General
3.2 MANET Routing Protocols
3.3 QoS Aware Routing
3.4 Conclusion

26
70 KB
5 4 DSDV AND DSR SIMULATION

4.1 General
4.2 Simulation Environment
4.3 Simulation Overview
4.4 Simulation Setup
4.5 DSR and DSDV Result Analysis
4.6 Measures of Interest
4.7 Mobility Analysis
4.8 Load Analysis
4.9 Conclusion

46
278 KB
6 5 SWARM INTELLIGENCE

5.1 General
5.2 Advantages of Swarm Intelligence
5.3 SI based Routing
5.4 Conclusion

73
103 KB
7 6 PROPOSED SOLUTION

6.1 Introduction
6.2 The Proposed Model
6.3 Traffic Classification
6.4 Routing Information
6.5 Conclusion

84
119 KB
8 7 RESULTS AND DISCUSSIONS

7.1 Network Topology
7.2 Simulations
7.3 Conclusion

100
706 KB
9 8 VALIDATION OF RESULTS

8.1 Introduction

133
80 KB
10 9 CONCLUSIONS AND FUTURE WORK

9.1 Conclusions
9.2 Future Work

142
13 KB
11

10

REFERENCES AND APPENDIX

 

144
54 KB