Energy efficient clustered deployment of Wireless Sensor Network using Particle Swarm Optimization

Abstract

Wireless Sensor Networks (WSN), is a group of low cost, low power, multifunctional and small size distributed networked sensors. These sensors work together to sense the environment with a little or no human intervention. These sensors are randomly deployed in the sensor field which brings the coverage problem. It is a unique problem and in maximizing coverage, the sensors need to be placed in a position such that the sensing capability of the network is fully utilized to ensure high quality of service. This can be achieved with minimum number of sensor nodes having maximum coverage in the network and the nodes are within the communication range. Particle swarm algorithm can be used to find the optimal positions of the sensors to determine the best coverage. PSO has been successfully used in numerous engineering applications like in training of neural networks to identify Parkinson's disease, extraction of rules from fuzzy networks, image identification, optimization of electric power distribution networks, structural optimization, inhabitant monitoring, environmental monitoring, monitoring deep oceans currents, smart home building and military applications among many others. This algorithm is an optimization technique which belongs to the fertile paradigm of swarm intelligence. It is a derivative free and is a very efficient global search algorithm with few algorithm parameters In this Project , we have tried to overcome the sensor placement problem in 3- dimensional terrain considering the problem of covering maximum volume with energy efficiency (using minimum amount of energy) to provide larger coverage and longevity to the network. The research is done in phases where in first relay structures are considered and then cluster structured of network are introduced to further reduce the dissipation of energy . PSO and its variants (APSO and DPSO) are successively applied to search for optimal placement of nodes ,thus providing a comparison/comparative study between the three.