Energy efficient strategies for object tracking in sensor networks: A data mining approach
In recent years, a number of studies have been done on object tracking sensor networks (OTSNs) 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 considered the object's movement behavior as randomness. In some real applications, however, the object movement behavior is often based on certain underlying events instead of randomness completely. In this paper, we propose a novel 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. To our best knowledge, this is the first work on mining the movement patterns associated with time intervals in OTSNs. Moreover, we propose novel location prediction strategies that utilize the discovered temporal movement patterns so as to reduce the prediction errors for energy savings. Through empirical evaluation on various simulation conditions and real dataset, TMP-Mine and the proposed prediction strategies are shown to deliver excellent performance in terms of scalability, accuracy and energy efficiency.