Energy efficient multi-criterion binary grey wolf optimizer based clustering for heterogeneous wireless sensor networks

Wireless sensor networks are constrained by limited energy resources of sensor nodes, which limits the efficiency and lifetime of the network. Clustering is a widely adopted strategy to improve the performance and longevity of the network by organizing it into smaller groups called clusters. Most swarm-based clustering algorithms aim to optimize a single objective, such as finding optimal cluster centers. However, in practical situations, simultaneous optimization of multiple objectives is often necessary to obtain promising cluster centers. In this study, we propose a multi-objective binary Grey wolf optimizer to find Pareto optimal clustering centers and achieve five objectives: maximizing overall cluster head energy, minimizing cluster compactness, minimizing the number of cluster heads, minimizing energy consumption from non-cluster head to cluster head transmission, and maximizing cluster separation. Our proposed approach outperforms other state-of-the-art evolutionary clustering protocols, such as SEP, IHCR, ERP, and BEECP, in terms of minimizing the percentage of dead nodes. Additionally, the stability period of the network is increased by 56% with our proposed approach. Simulation outcomes demonstrate improved performance in terms of total residual energy, number of elected cluster heads, and network lifetime.