An energy efficient GA-based algorithm for clustering in wireless sensor networks

Abstract

Energy efficient clustering has gained enormous attention in wireless sensor networks (WSNs) in the last few decades. In clustered WSNs, cluster heads (CHs) bear some additional load such as data sensing, data aggregation and sending data to base station. However, improper formation of clusters can overload some CHs and resulting higher energy consumption that may lead to deaths of such CHs. Load balanced clustering for large scale WSNs is an NP-hard problem. Evolutionary algorithms can be applied to solve such problem in a fast and efficient way. In this paper, we propose a genetic algorithm (GA) based load balancing clustering algorithm for WSNs to prolong the lifetime of the network. The proposed clustering algorithm, binds each sensor node with a CH via single or multi-hop communication, where a CH transmit all data packets of its cluster members to the base station. We perform extensive experiments on the proposed algorithm to evaluate the performance of the proposed algorithm along with some other related clustering algorithm in terms of different metrics such as network lifetime, energy efficiency etc.