Dynamic Cluster Members Scheduling for Target Tracking in Sensor Networks

Abstract

The sensor scheduling for energy-efficient target tracking with high performance in wireless sensor networks (WSNs) is a dilemma problem. By analyzing the intrinsic relationship between tracking performance and energy consumption, we cast the scheduling problem of WSN as the optimal policy problem of partially observable Markov decision process (POMDP), and propose a dynamic cluster members scheduling (DCMS) algorithm to solve the tradeoff between tracking performance and energy consumption. First, we exploit an election method, based on the optimal mixed weights of the signal strength and the residual energy of node, to choose the cluster head node. Then, we seem each cluster members an agent, and model the scheduling problem of cluster members by POMDP. At last, a point-based online value iteration algorithm is presented to solve the DCMS to generate the collaboration strategy of sensor cluster members dynamically. The simulation results show that the proposed approach can improve the accuracy of target tracking, decrease the energy consumption of sensor nodes, and prolong the lifetime of sensor networks.