Load balanced energy-aware genetic algorithm clustering in wireless sensor networks

Abstract

Extending lifetime of wireless sensor networks is a major issue in WSNs due to their energy resource constraint. To solve this problem, various approaches have been proposed recently. Clustering is an effective topology control technique, among these approaches. This paper introduces a novel Load balanced Energy-aware Genetic Algorithm Clustering (LEGAC) technique applied in WSNs. This technique is implemented at the base station. In the proposed technique, two-stage GA is employed to select optimal set of clusters. In the first stage, the technique picks up the optimal cluster heads. In the second stage, this technique assigns appropriate cluster members to these cluster heads. Moreover, intra-cluster distance is optimized tacking into account load-balancing constraint. The objective is to minimize energy consumption. The performance of this technique is compared with similar techniques, i.e., UCFIA-unequal clustering algorithm using Fuzzy logic, GCA-multi-hop clustering, and SCP-load balanced staggered clustering protocol. Simulation results show that LEGAC outperforms all these techniques in the same set up in terms of network lifetime, energy efficiency and network coverage.