A Multi-objective Cluster Head Selection Optimization Algorithm for Industrial Wireless Sensor Networks

Industrial Wireless Sensor Networks (IWSNs) have come to be widely used for collecting industrial information due to their low cost, rapid deployment, and high service quality. Clustering sensors together extends the lifetime of IWSNs is an essential part of improving their energy efficiency and network stability. In this paper, we propose a novel Multi-Objective Cluster Head Selection Optimization Model (MOCHSOM) to improve the selection of cluster heads by taking into account multiple factors critical to the IWSNs lifetime. Specifically, our model minimizes the total network energy consumption, balances the network cluster energy consumption, and maximizes the network node alive time. Moreover, we employ an Evolutionary Algorithm using Reference-point Based Non-dominated Sorting Approach (NSGA-III) to optimize the MOCHSOM model further. The experimental results show that our proposed algorithm significantly improves the network lifetime when compared with conventional clustering algorithms.