Energy Efficient Multi-Clustering Using Grey Wolf Optimizer in Wireless Sensor Network

The most important challenge in wireless sensor networks is to extend the network lifetime, which is directly related to the energy consumption. Clustering is one of the well-known energy-saving solutions in WSNs.  To put this in perspective, the most studies repeated cluster head selection methods for clustering in each round, which increases the number of sent and received messages. what's more, inappropriate cluster head selection and unbalanced clusters have increased energy dissipation. To create balanced clusters and reduce energy consumption, we used a centralized network and relay nodes, respectively. Besides, we applied a metaheuristic algorithm to select the optimal cluster heads because classical methods are easily trapped in local minimum. In this paper, the Grey Wolf Optimizer(GWO), which is a simple and flexible algorithm that is capable of balancing the two phases of exploration and exploitation is used. To prolong the network lifetime and reduce energy consumption in cluster head nodes, we proposed a centralized multiple clustering based on GWO that uses both energy and distance in cluster head selection. This research is compared with classical and metaheuristic algorithms in three scenarios based on the criteria of "Network Lifetime", "Number of dead nodes in each round" and "Total Remaining Energy(TRE) in the cluster head and relay nodes. The simulation results show that our research performs better than other methods. In addition, to analyze the scalability, it has been evaluated in terms of "number of nodes", "network dimensions" and "BS location". Regarding to the results, by rising 2 and 5 times of these conditions, the network performance is increased by 1.5 and 2 times, respectively.