Under resource-constrained conditions, sensor selection scheme based on particle swarm optimization is proposed
for collaborative target tracking in large-scale wireless sensor networks. By using Gaussian particle filtering and covariance
intersection, the proposed scheme can predict the target’s state next time. Based on the state prediction, this scheme can select
the cluster member nodes, design the cost function for communication energy consumption, and obtain the optimal cluster
head node by particle swarm optimization. Simulation results show that this scheme reduces the computational complexity,
and keeps the good tracking performance.