Dual prediction-based reporting for object tracking sensor networks

As one of the wireless sensor network killer applications, object tracking sensor networks (OTSNs) disclose many opportunities for energy-aware system design and implementations. We investigate prediction-based approaches for performing energy efficient reporting in OTSNs. We propose a dual prediction-based reporting mechanism (called DPR), in which both sensor nodes and the base station predict the future movements of the mobile objects. Transmissions of sensor readings are avoided as long as the predictions are consistent with the real object movements. DPR achieves energy efficiency by intelligently trading off multihop/long-range transmissions of sensor readings between sensor nodes and the base station with one-hop/short-range communications of object movement history among neighbor sensor nodes. We explore the impact of several system parameters and moving behavior of tracked objects on DPR performance, and also study two major components of DPR: prediction models and location models through simulations. Our experimental results show that DPR is able to achieve considerable energy savings under various conditions and outperforms existing reporting mechanisms.