Partitioned uneven cluster routing algorithm based on gray wolf optimization in WSNs

Abstract

WSNs have various uses across numerous industries and are one of the essential technologies of modern life. Energy consumption is the issue that has drawn the greatest attention and still has to be resolved because the nodes that comprise WSNs have a limited amount of energy. Numerous factors influence energy consumption, and our algorithm design considerations are centered on extending the network lifetime and energy efficiency through the resolution of imbalance and hotspot issues related to WSNs clustering. Because of this, we suggest a partitioned uneven cluster routing algorithm based on gray wolf optimization. To find the ideal cluster head, we first divide the network into areas with distinct important influence factors, then we improve the final cluster head election function and the candidate cluster head competition radius. Subsequently, to reduce the energy consumption resulting from multiple rounds of clustering, similarity determination is introduced. Finally, the optimal transmission path in the multi-hop process is obtained by combining the Gray Wolf optimization algorithm with the relay node selection function. Simulation results show that the network lifetime of the proposed algorithm is extended by 54.6 %, 46.2 %, 58.6 %, and 18.5 % compared to LEACH, DEBUC, LEACH-EDP, and LEACH-IM, respectively. The energy efficiency of the proposed algorithm is extended by 40.8 %, 7.1 %, 22.7 %, and 34.0 %, respectively. The proposed algorithm significantly extends the network lifetime and improves the energy efficiency of the network.