Energy Efficient Cluster Head Selection for Internet of Things

Abstract

ABSTRACT Recently, Internet of Things (IoT) devices are highly utilized in diverse fields such as environmental monitoring, industries, and smart home, among others. Under such instances, a cluster head is selected among the diverse IoT devices of wireless sensor network (WSN) based IoT network to maintain a reliable network with efficient data transmission. This article proposed a novel method with the combination of Gravitational Search Algorithm (GSA) and Artificial Bee Colony (ABC) algorithm to accomplish the efficient cluster head selection. This method considers the distance, energy, delay, load, and temperature of the IoT devices during the operation of the cluster head selection process. Furthermore, the performance of the proposed method is analyzed by comparing with conventional methods such as Artificial Bee Colony (ABC), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and GSO algorithms. The analysis related to the existence of the number of alive nodes, convergence estimation, and performance in terms of normalized energy, load, and temperature of the IoT devices are determined. Thus the analysis of our implementation reveals the superior performance of the proposed method.