Energy-Efficient Grasshopper Optimization Algorithm (EEGOA) Based Cluster Head Election for WSNs

Abstract

The lifespan of a wireless sensor network (WSN) is shortened and energy efficiency declines as a result of unequal energy usage. The goal of clustering approach is to balance energy depletion while minimizing data redundancy in order to increase energy efficiency. Two different types of jobs, the Cluster Head (CH) and the Cluster Member (CM) coexist in each cluster. The Grasshopper Optimization Algorithm (GOA) is used in this article to encourage energy balance throughout the CH election phase. Best of the authors' knowledge, this stays the first occasion that  systematic GOA had any influence upon the CH election. In order to achieve this, the Calinski-Harabasz index is used to take the best CH. The CH is calculated using this algorithm in order to maintain energy stability within each cluster. The energy effectiveness of the system was assessed using extensive simulations. Evaluation metrics show it is effective in increasing energy efficiency and extending network time in WSNs as compared to typical clustering strategies.