Mining Region-Based Movement Patterns for Energy-Efficient Object Tracking in Sensor Networks

In recent years, a number of studies have been done on object tracking sensor networks (OTSNs) due to the wide applications. One important issue in OTSNs is the energy saving strategy for object tracking and most existing solutions are based on statistical methods. In this paper, we propose a data mining-based approach for energy-efficient object tracking in OTSNs. First, a data mining methodology named RM-mine is proposed for discovering the region-based movement patterns of moving objects in an OTSN. Moreover, we also propose the corresponding prediction strategies for tracking objects in energy-efficient way. Through empirical evaluations on various simulation conditions, RM-mine and the proposed prediction strategies are shown to deliver excellent performance in terms of scalability, accuracy and energy efficiency.