Energy Efficient Clustering in IOT-Based Wireless Sensor Networks using Whale Optimization Algorithm

One of the most critical challenges of wireless sensor networks is the limited energy of the nodes, which has tried to manage energy consumption in these networks by using more accurate clustering. So far, many methods have been proposed to increase the accuracy of clustering, which reduces the energy consumption of nodes and thus increases network throughput. In this paper, we propose a method for clustering wireless sensor networks using the whale optimization algorithm, which results in increased throughput in these networks. Although much work has been done in this area in terms of energy, some do not have good throughput. Therefore, in this paper, a clustering method based on the whale optimization algorithm is proposed. Features of this algorithm include easy implementation, providing high- quality solutions, quick convergence, and the ability to escape from local minima. Also, in terms of clustering, in addition to paying attention to energy consumption, has appropriate throughput. In the proposed method, the Euclidean distance is used to assign data to the cluster and determine the cluster centers by the whale optimization algorithm. In other words, concentrated clusters are created. Then, according to the two remaining energy parameters and the distance of the nodes to the centers of the cluster, two clusters are selected. To evaluate the research, we have used MATLAB software and compared the proposed method with one of the latest works. The results show an improvement in throughput and comparable in terms of energy