A Glowworm Swarm Optimization Based Clustering Algorithm with Mobile Sink Support for Wireless Sensor Networks

Abstract

In recent years, many energy efficient algorithms and routing protocols have been proposed for wireless sensor networks (WSNs) with the object to balance the energy consumption and maximize the network lifetime. It has been proved that utilizing clustering technique and adding sink mobility into sensor networks can bring in new opportunities to improve energy efficiency for WSNs. Glowworm Swarm Optimization (GSO), which belongs to the swarm intelligence field inspired by simulated experiments of the lighting worms' behavior, is also an effective method to improve network performance. In this paper, we proposed a clustering algorithm based on GSO with mobile sink (CAGM) for WSNs, which tries to combine GSO algorithm, clustering technique and sink mobility strategies together to extend network lifetime and improve network performance. We first divide the whole network into several clusters and select cluster heads of each cluster by utilizing GSO algorithm. After then, cluster heads collect data from their member nodes and do data fusion. In order to improve network performance, a mobile sink was used to roam about the network and collect data from cluster heads through really short communication range. Extensive simulation results show that our proposed algorithm can efficiently prolong the network lifetime of WSNs.