OPTIMIZATION OF CLUSTER HEAD SELECTION AND ENHANCING ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS USING NOVEL METAHEURISTIC ALGORITHMS

Abstract

Clustering-based routing is preferred to support fault tolerance, load balancing, reliable communication, and to prolong the network lifetime in a wireless sensor network (WSN). The low-energy adaptive clustering hierarchy (LEACH) is the most popular routing technique, introduced for the first time for homogeneous WSNs. However, the random selection of cluster heads (CHs) in LEACH protocols results in poor performance in real network deployments due to the faster rate of energy depletion at CHs. The dynamic selection of CHs based on a heuristic approach can minimize the energy consumption at CHs and enhance the network lifetime. In this paper, a metaheuristic algorithm called grey wolf optimization (GWO) and its enhanced versions are proposed in selecting the optimal CH. The fitness function is defined based on sink distance to CH and residual energy at the sensor node. The optimal values of fitness function give an efficient CH selection and cost-effective routing. The primary goal of this paper is to maximize the network lifetime of WSNs by optimal selection of CHs using the improved GWO (IGWO) algorithm. The proposed IGWO-based LEACH protocol confirmed the optimal selection of CH with minimum energy consumption, resolved premature convergence, and enhanced the network lifetime by balancing the number of alive and dead nodes in WSN.