Sensor Selection Method for Target Tracking based on Hybrid Binary Whale Optimization Algorithm in Wireless Sensor Networks

Abstract

Due to various resource limitations of wireless sensor networks, selecting the appropriate sensors to participate in target tracking is necessary. The real measurement data effects the target tracking accuracy, so this paper proposes a hybrid binary whale optimization algorithm (HBWOA). The proposed algorithm is used to solve the sensor selection model constructed using the actual measurement data and conditional posterior Cramer-Rao lower bound (CPCRLB). The proposed algorithm adopts the position update principle of V-shaped function, proposes nonlinear dynamic adaptive convergence factor to adaptively adjust the exploration and exploitation phase of algorithm, and proposes dynamic disturbance weight enhances the searchability in the exploration stage, which improves the convergence performance and the accuracy of algorithm search in the exploitation phase, avoids local optimization and improves the accuracy of algorithm search in the exploitation phase. Experimental simulations show that the proposed algorithm has better performance on target tracking problem than binary particle swarm optimization (BPSO), binary grey wolf optimization (BGWO), binary salp swarm algorithm (BSSA), and The traditional binary whale optimization algorithm (BWOA_s), it has a better balance between the exploration phase and the exploitation phase.