A Novel Hybridization of ML Algorithms for Cluster Head Selection in WSN

Abstract

Generally, Wireless Sensor Networks (WSNs) are infrastructure-less networks with thousands of sensor nodes that sense or monitor the physical and environmental changes and forward the collected data to a central node. Besides, WSN has become the most efficient technology for handling Internet of Things (IoT) devices. Still, challenges such as node failures, high traffic among the nodes, link failures, etc., limit the performance of WSNs. To solve the challenges in WSN, this paper aims to develop a novel non-uniform clustering model, where the Cluster Heads (CHs) are selected based on the candidate CH selection strategy that transfers the data. Moreover, unbalanced energy utilization and data redundancy are eliminated via multi-hop communication. For attaining the non-uniform clustering model, the routing among the data packets is done by the efficiency of the hybridization of the Machin Learning (ML) algorithms viz Genetic Algorithm (GA) and Lion Algorithm (LA) with the consideration of energy, cost, time, network lifetime, and data accuracy. Finally, the performance of the proposed model is verified and validated through a comparative study with the existing models.