Energy Efficient Object Tracking in Sensor Networks by Mining Temporal Moving Patterns

Object tracking sensor networks (OTSNs) have received extensive attentions for researches in recent years due to the wide applications. One important research issue in OTSNs is the energy saving strategy in considering the limited power of sensor nodes. The past studies on energy saving in OTSNs usually considered the movement behavior of objects as randomness. However, in some real applications, the object movement behavior often carries certain patterns instead of randomness completely. In this paper, we propose an efficient data mining algorithm named TMP-Mine with a special data structure named TMP-Tree for efficiently discovering the temporal movement patterns of objects in sensor networks. Moreover, we propose novel location prediction strategies that employ the discovered temporal movement patterns so as to reduce the prediction errors for energy saving. Through empirical evaluation on simulated, TMP-Mine and the proposed prediction strategies are shown to deliver excellent performance in terms of scalability, accuracy and energy efficiency.