Probability-Based Prediction and Sleep Scheduling for Energy-Efficient Target Tracking in Sensor Networks

Abstract

A surveillance system, which tracks mobile targets, is one of the most important applications of wireless sensor networks. When nodes operate in a duty cycling mode, tracking performance can be improved if the target motion can be predicted and nodes along the trajectory can be proactively awakened. However, this will negatively influence the energy efficiency and constrain the benefits of duty cycling. In this paper, we present a Probability-based Prediction and Sleep Scheduling protocol (PPSS) to improve energy efficiency of proactive wake up. We start with designing a target prediction method based on both kinematics and probability. Based on the prediction results, PPSS then precisely selects the nodes to awaken and reduces their active time, so as to enhance energy efficiency with limited tracking performance loss. We evaluated the efficiency of PPSS with both simulation-based and implementation-based experiments. The experimental results show that compared to MCTA algorithm, PPSS improves energy efficiency by 25-45 percent (simulation based) and 16.9 percent (implementation based), only at the expense of an increase of 5-15 percent on the detection delay (simulation based) and 4.1 percent on the escape distance percentage (implementation based), respectively.