Energy Aware Clustering System for Wireless Sensor Networks utilizing Rider Sunflower Optimization Approach

Abstract

Wireless Sensor Networks (WSN) are spatially disseminated sensors that are utilized for monitoring physical or environmental factors, like sound, temperature, pressure, and so on, to collectively drive their information from the networking to the base station. The WSN is composed of hundreds or thousands, where all the nodes are interconnected with other Sensor Node (SN). Clustering is the most popular topology management technique in WSN, grouping nodes to manage them or execute different tasks in a distributed manner, like resource management. It includes grouping sensors and selecting Cluster Heads (CHs) for every cluster. Therefore, this study presents a new Rider Sunflower Optimizing Model-Based Energy Aware Clustering Approach (RSFOA-EACA) for WSNs. The prime goal of the RSFOA-EACA technique is in the optimum selection of CH for data transmission in the WSN. With Rider Optimization Algorithm (ROA) and Sunflower Optimization (SFO) incorporation, the RSFOA-EACA technique mainly depends upon the RSFOA. Furthermore, the RSFOA-EACA algorithm derives a Fitness Function (FF) by the computation of distance, Residual Energy (RE), Node Degree (ND), and network coverage. The CH selecting enables proper inter-cluster transmission in the network. The experimental analysis of the RSFOA-EACA method is investigated by implementing a sequence of simulations. The simulation values emphasized the promising energy efficiency outcomes of the RSFOA-EACA approach.