Predictive Target Detection and Sleep Scheduling for Wireless Sensor Networks

In tracking a mobile target using wireless sensor networks (WSN), efficient sleep scheduling is needed to reduce energy consumption without severely deteriorating the tracking performance. Conventional prediction-based sleep scheduling techniques require position, velocity and even acceleration of the target in order to wake up the nodes according to the next predicted location of the target. In these data, effects of sensor faults and uncertainties are included, which can degrade the overall performance. In order to address this issue, we propose a new predictive target detection algorithm which uses only local measurement, eliminating the need for communication among nodes. The proposed prediction algorithm provides a prediction of the number of detections that will occur starting from the moment when a target enters the sensor coverage area until it leaves. This algorithm is evaluated via experiments including several movement scenarios of the target. The results show that the algorithm accurately reflects the remaining number of target detections until the target leaves. In addition, the prediction algorithm is applied to sleep scheduling and compared with the circle-based sleep scheduling. Our scheduling strategy improves energy efficiency with a very small, negligible loss in the tracking performance.