Energy-Balanced and Stability-Oriented Clustering Algorithm for Fragment Velocity Measurement Networks

To address the energy limitations, long-term operation demands, and load imbalance in fragment velocity measurement wireless sensor networks, this paper proposes an Energy-Balanced and Stability-Oriented Grey Wolf Optimization (EBSIGWO) algorithm. The algorithm employs a multi-objective fitness function that jointly considers residual energy, intra-cluster load balance, and long-term communication cost, ensuring both energy efficiency and clustering stability. A dynamic elite ratio strategy is further introduced to adaptively balance global exploration and local exploitation, thereby mitigating cluster-head overload and slowing energy depletion. Simulation results show that EBSIGWO significantly extends network lifetime compared with LEACH, HEED, GWO, and FIGWO, improving the half-node-death (HND) round by 518.0%, 200.1%, 111.2%, and 30.5%, respectively. Moreover, EBSIGWO reduces energy variance and slows energy consumption, demonstrating superior energy balance and overall efficiency. These results indicate that EBSIGWO provides an effective solution for reliable fragment velocity measurement applications.