Performance Analysis of Hybridization of [PIO-GSO] Algorithms in Wireless Sensor Networks

Abstract

In wireless sensor networks (WSN), clustering is treated as an energy efficient technique employed to achieve augmenting network lifetime. But, the process of cluster head (CH) selection for stabilized network operation and prolonged network lifetime remains a challenging issue in WSN. In this research, presents a novel Hybridization of Pigeon Inspired with Glowworm Swarm Optimization (HPIGSO) algorithm based clustering innovation in WSN. This innovative 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, cluster head selection and cluster construction. Once the nodes are deployed, the initialization process takes place. Followed by, Base Station (BS) executes the HPIGSO algorithm and selects the cluster heads effectively. Subsequently, nearby nodes joins the cluster head and becomes cluster members, thereby cluster construction takes place. Finally, the cluster members send the data to cluster heads which is then forwarded to the base station via inter-cluster communication. The performance of the proposed HPIGSO method has been evaluated and compared with QOGSO, PIOA-DS, ALO, GOA and FFOA. Finally the proposed HPIGSO algorithm provides prolonged the lifetime of WSN over the existing clustering techniques.