We present a probability-based target prediction and sleep scheduling protocol (PPSS) to improve the efficiency of proactive wake up and enhance the energy efficiency with limited loss on the tracking performance. With a target prediction scheme based on both kinematics rules and theory of probability, PPSS not only predicts a target’s next location, but also describes the probabilities with which it moves along all the directions.
PPSS provides a directional probability as the foundation of differentiated sleep scheduling in a geographical area. Then, based on the prediction results, PPSS enhances energy efficiency by reducing the number of proactively awakened nodes and controlling their active time in an integrated manner.
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. Probability-Based Prediction and Sleep Scheduling for Energy-Efficient Target Tracking in Sensor Net
Existing method in a minimal contour tracking algorithm (MCTA) that reduces energy consumption for tracking mobile targets in wireless sensor networks in terms of sensing and communication energy consumption MCTA conserves energy by letting only a minimum number of sensor nodes participate in communication and perform sensing for target tracking. MCTA uses the minimal tracking area based on the vehicular kinematics.
The modeling of target’s kinematics allows for pruning out part of the tracking area that cannot be mechanically visited by the mobile target within scheduled time. So, MCTA sends the tracking area information to only the sensor nodes within minimal tracking area and wakes them up.
Circle-based tracking area scheme uses less number of sensors for tracking in both communication and sensing without target missing. Through simulation, we show that MCTA outperforms the circle-based scheme with about 60% to 70% energy using under certain ideal situations.
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.