Energy efficient clustering protocol based on improved metaheuristic in wireless sensor networks

Energy efficient clustering is a well accepted NP-hard optimization problem in Wireless sensor networks (WSNs). Diverse paradigm of Computational intelligence (CI) including Evolutionary algorithms (EAs), Reinforcement learning (RL), Artificial immune systems (AIS), and more recently, Artificial bee colony (ABC) metaheuristic have been used for energy efficient clustering in WSNs. Due to ease of use and adaptive nature, ABC arose much interest over other population-based metaheuristics for solving optimization problems in WSNs. However, its search equation, which is comparably poor at exploitation and require storage of certain control parameters, contributes to its insufficiency. Thus, we present an improved Artificial bee colony (iABC) metaheuristic with an improved solution search equation to improve its exploitation capabilities. Additionally, in order to increase the global convergence of the proposed metaheuristic, an improved population sampling technique is introduced through Student′s−t distribution, which require only one control parameter to compute and store, hence increase efficiency of proposed metaheuristic. The proposed metaheuristic maintain a good balance between exploration and exploitation search abilities with least memory requirements, moreover the use of first of its kind compact Student′s−t distribution, make it suitable for limited hardware requirements of WSNs. Further, an energy efficient clustering protocol based on iABC metaheuristic is introduced, which inherit the capabilities of the proposed metaheuristic to obtain optimal cluster heads (CHs) and improve energy efficiency in WSNs. Simulation results shows that the proposed clustering protocol outperforms other well known protocols on the basis of packet delivery, throughput, energy consumption, network lifetime and latency as performance metric.