Particle Swarm Optimization based Cluster Head Election Approach for Wireless Sensor Network

Abstract

Wireless sensor networks comprise several sensor nodes being supported by limited capacity battery source. The network hierarchy may be chosen as per required applications, such as clustered arrangement. Cluster heads play a major role in clustering Wireless Sensor Network. Particle swarm optimization (PSO) is one of the swarm based intelligence methods for locating optimum solution by imitating the behavior of flocks of birds and fish schooling. PSO is based on the movement and intelligence of swarms. Social learning factor can achieve better convergence speed and particle reselection mechanism reduces the chances of being trapped in local maximum. In this paper cluster head selection based on PSO approach is proposed. The performance of the proposed approach is compared with low energy adaptive cluster Hierarchy (LEACH). Simulation result shows that proposed approach outperforms LEACH in terms of first node died (FND), total data received by base station and energy consume per round. The simulation has been carried out over different network size and with different number of cluster heads and it is clearly seen from the results that the proposed approach outperform LEACH in large network size also.

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“…It works through initializing a population of random solutions and searching for the optima by updating generations. All particles have a fitness value that is evaluated with the help of the fitness function and have a velocity that directs the movement of the particles [21]. Particle swarm optimization is initialized through a set of random solutions (particles), then searches for optimal by the generational update.…”