Improved Artificial Bee Colony Optimization-Based Clustering Technique for WSNs

Abstract

The core objective of controlling topology, conserving energy and extending the life cycle in sensor networks is highly indispensable for maximizing its lifetime. Meta-heuristic optimization algorithms are found to be optimal in facilitating the process of cluster head election and clustering. An Improved Artificial Bee Colony Optimization-based Clustering Technique (IABCOCT) is propounded by deriving the merits of Grenade Explosion and Cauchy operator for ensuring optimal clustering process and election of cluster heads. The potential of Grenade Explosion and Cauchy operator are embedded in the Onlooker Bee and scout bee phase for phenomenal improvement in the degree of exploitation and exploration of searching that aids in the optimal election of cluster heads. This incorporation of Grenade Explosion and Cauchy operator in the clustering process maximizes the rate of cluster head election for reducing energy consumption in sensor nodes that enhances lifetime of the network. The potential of IABCOCT is studied through simulation experiments and its performance is investigated against Enhanced Particle Swarm Optimization Technique (EPSOCT), Hierarchical Clustering-based Cluster Head Election (HCCHE) and Competitive Clustering Technique (CCT). The simulation results of IABCOCT prove that the mortality rate of sensor nodes is minimized for enhancing network lifetime. The results deduce that the relative energy, dropping ratio, Mean energy consumption and Network Routing Overhead is better than EPSOCT, HCCHE and CCT under the impact of varying number of rounds.