Self-Adaptive Intelligent Routing in Dynamic WSN using Natural Inspired Computing

Mobile Adhoc Networks are designed dynamically without any infrastructure and each node is accountable for routing information amongst them. In MANET's, the network topology dynamically variations over time to time due to energy preservation or changes in node position. Thus both routing proble...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2017-08, Vol.225 (1), p.12123
Main Authors: Mustary, Nareshkumar R., Phanikumar, S.
Format: Article
Language:English
Subjects:
Changing environments
Constriction Factor
Design optimization
Inertia Weight
MANET's
Mobile ad hoc networks
Mobile computing
Mutation Operator
Network topologies
Optimization
Particle swarm optimization
PSO
Wireless sensor networks
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Summary:Mobile Adhoc Networks are designed dynamically without any infrastructure and each node is accountable for routing information amongst them. In MANET's, the network topology dynamically variations over time to time due to energy preservation or changes in node position. Thus both routing problem turn out to be dynamic optimization problem in MANET's. Hence it is crucial to design solution for the optimization problem is to quickly adopt to changing environment and produce high quality optimization using Modified Particle Swarm Optimization. The Particle Swarm Optimization is effective in determining optimal solutions in fixed locations, but it suffered from poor performance in locating a changing extreme. It was also necessary to impose a maximum value Vmax to avoiding the particle exploded because of there was no exist a mechanism for controlling the velocity of a particle. PSO searches wide areas effectively, but difficult to search in local precision. Hence, introduced a control parameter called the inertia weight, "w", to damp the velocities over time, allowing the swarm to converge more accurately and efficiently.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/225/1/012123