Energy efficient load balancing approach for avoiding energy hole problem in WSN using Grey Wolf Optimizer with novel fitness function

Abstract

In general architecture of Wireless Sensor Networks (WSNs), gateways far from the Base Station (BS) communicate with the BS via the gateways close to the BS. The energy of gateways which are close to the BS drains faster due to the heavy traffic load. This leads to the energy hole problem around the BS. Therefore, proper clustering of sensor nodes and routing of data are essential for efficient conservation of energy and for avoiding inadvertent network failure due to a power drain. In this paper, we apply the Grey Wolf Optimization (GWO) approach for energy-efficient clustering and routing in WSN. Also, we propose two novel fitness functions for clustering and routing problems. The fitness function for routing is formulated such that overall distance traversal and number of hops are minimized. The fitness function for clustering distributes the overall load according to the distance of gateways to the BS. The proposed GWO-based approach is resulted with higher values of both clustering and routing fitness functions as compared to the existing algorithms, namely, genetic algorithm, particle swarm optimization and multi-objective fuzzy clustering.