Hybridization of Pigeon inspired with glowworm' swarm optimization based clustering technique in wireless sensor networks

Abstract

In wireless sensor networks (WSN), clustering is treated as an energy efficient technique employed to achieve maximum network lifetime. But, the process of cluster head (CH) selection for stabilized network operation and prolonged network lifetime remains a challenging issue in WSN. To resolve this issue, this paper presents a new hybridization of pigeon inspired with glowworm swarm optimization (HPIGSO) algorithm based clustering technique in WSN. The proposed HPIGSO algorithm integrates the good characteristics of pigeon inspired optimization (PIO) algorithm and glowworm swarm optimization (GSO) algorithm. The proposed algorithm operates on three major stages namely initialization, CH selection and cluster construction. Once the nodes are deployed, initialization process takes place. Followed by, base station (BS) executes the HPIGSO algorithm and selects the CHs effectively. Subsequently, nearby nodes joins the CH and becomes cluster members (CMs), thereby cluster construction takes place. Finally, the CMs send the data to CHs which is then forwarded to BS via inter-cluster communication. The proficient performance of the HPIGSO method has been evaluated and the results portrayed that the HPIGSO algorithm prolonged the lifetime of WSN over the existing clustering techniques.