Tracking and Predicting Moving Targets in Hierarchical Sensor Networks

Abstract

Target tracking is an important application of newly developed wireless sensor networks (WSN). Much work has been done on this topic using a plane network architecture. We propose a scheme, namely hierarchical prediction strategy (HPS), for target prediction in hierarchical sensor networks. The network is divided into clusters, which are composed of one cluster-head and many normal nodes, by Voronoi division. For an existing target, cluster-heads only selectively activate nearby sensor nodes to perform tracking. Moreover, Recursive Least Square technique is used to predict the target trajectory and help activate next-round sensor nodes. Extended simulations show the properties of the proposed network architecture and the efficiency of the prediction scheme.