A Prediction-based Energy-conserving Approximate Storage and query processing schema in object-tracking sensor networks

Energy efficiency is one of the most critical issues in the design of wireless sensor networks. In object-tracking sensor networks, the data storage and query processing should be energy-conserving by decrease the message complexity. In this paper, by figuring out the shortcomings of EASE, a Prediction-based Energy-conserving Approximate StoragE (P-EASE) is proposed, which reduce the query error of EASE by changing its approximate area and adopting predicting model without increasing the cost. The theoretical analyses illuminate the correctness and efficiency of the P-EASE. And simulation experiments are conducted under semi-random walk and random waypoint mobility which compares the query error, message complexity, total energy consuming and hotspot energy consuming to validate that P-EASE is more energy-conserving than EASE and has less query error as well.