A Dual Cluster Head Hierarchical Routing Protocol for Wireless Sensor Networks Based on Hybrid Swarm Intelligence Optimization

Clustering routing is one of the most prevailing approaches for saving energy in Wireless Sensor Networks (WSNs). However, many existing clustering protocols have the problem of node premature death. This is primarily due to the frequent selection of some advantageous nodes as cluster heads (CHs), which bear the responsibility of data aggregation and forwarding, resulting in higher energy consumption. To address this problem, this paper proposes a dual-CH clustering routing model and introduces an interval-based CH reelection mechanism to reduce the energy consumption associated with frequent cluster formation. In addition, a comprehensive consideration of residual energy and base station distance in CH election facilitates the design of an improved hybrid swarm intelligence optimization algorithm termed GWOA-CH, which combines Gray Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA) to obtain a more effective CH election scheme. To verify the effectiveness of the proposed algorithm, extensive experiments are conducted between GWOA-CH and some state-of-the-art WSNs routing protocols, including ModifyGA, NCOGA, ARSH-FATICHS and MMROR. Experimental results demonstrate the superior energy saving capabilities of the proposed clustering protocol compared to its alternatives.