Leveraging Gradient based Optimization based Unequal Clustering Algorithm for Hotspot Problem in Wireless Sensor Networks

Abstract

Wireless sensor networks (WSNs) serve as the basic unit of the Internet of Things (IoT). Because of their low prices and potential use, in recent years, wireless sensor networks (WSNs) have garnered attention for various uses. Then sensor nodes (SN) can prepared with limited battery is critical energy utilization be monitored closely. Hence, reducing the node energy utilization is obviously vital to extending the network lifespan. Clustering is an effectual manner for diminishing energy utilization in WSNs. In a multi-hop clustered network condition, every SN transfers data to its individual cluster head (CH), and the CH gathers the information from its member nodes and relays it to base station (BS) using other CHs. Conversely, the “hotspot” issue is inclined to take place in clustered WSNs while CHs near the BS are heavier intercluster forwarding tasks. In this article, we leverage Gradient based Optimization based Unequal Clustering Algorithm for Hotspot Problem (GBOUCA-HP) technique in the WSN. The GBOUCA-HP technique is applied to get rid of the unequal clustering process in the WSN using metaheuristic algorithms. The GBOUCA-HP technique focuses on the optimization of energy usage, resolving hot spots, and extending the network lifespan. In the GBOUCA-HP technique, the GBO algorithm is based on two concepts such as diversification and intensification search and the gradient‐based Newton’s phenomena. Moreover, the GBOUCA-HP technique adaptive selects the CHs with varying cluster sizes for diverse energy levels and computation abilities of the nodes. The widespread set of simulations and evaluations shows the effective performance of the GBOUCA-HP technique. The GBOUCA-HP technique is found to be a significant approach to tackling the hotspot issue in the WSN with the intention of decreasing energy consumption optimization and enhancing network efficiency.