EEMC: Energy Efficient Multi-Clustering Using Grey Wolf Optimizer in WSNs

Abstract

The most significant 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. In this study we proposed a centralized multiple clustering based on GWO called “EEMC” which uses both energy and distance in cluster head selection in order to prolong the network lifetime and reduce energy consumption in cluster head nodes. The EEMC 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 in EEMC”. The simulation results illustrate that the EEMC performs better than other methods. In addition, to analyze the scalability, the EEMC 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.