Energy balanced clustering protocol using particle swarm optimization for wireless sensor networks

Abstract

In a large scale Wireless Sensor Networks (WSNs), designing an energy balanced clustering protocol has become a challenging research issues. This is due to fact that design of an energy-balanced clustering for maximizing the network lifetime of WSNs is a NP-hard problem. For solving this NP-hard problem, many meta-heuristic approach based clustering protocols are proposed in the recent years. However, these existing clustering protocols suffer from unbalanced energy consumption problem. In this problem, cluster heads are not uniformly distributed and overloaded cluster heads die out faster than under-loaded cluster heads. In order to solve this problem, we have proposed an energy balanced clustering protocol using particle swarm optimization called EBC-PSO. In the proposed protocol, we have used a novel multi-objective fitness function which contains three constraints such as average intra-cluster distance, residual energy and average cluster size. A detailed evaluation and performance comparison of the EBC-PSO with the three most popular protocols such as LEACH, PSO-ECHS, and E-OEERP are included.