Adaptive Tracking in Energy Sensitive Distributed Wireless Sensor Networks
We study the problem of tracking moving targets using distributed Wireless Sensor Networks (WSNs) in which sensors are deployed randomly. Prediction-based techniques are a commonly used strategies to reduce the power usage of energy sensitive wireless sensor networks where sensor nodes are battery-powered. However, due to the uncertainty and unpredictability of real-world targets' motion, the power efficiency of tracking and the accuracy of prediction are reduced. The tracking algorithm must adapt to the real-time changes in velocity and direction of a moving target. In this paper, we proposed a novel energy efficient tracking algorithm called Predict-and-Mesh (PaM) which is suitable for energy sensitive distributed wireless tracking systems. Making use of the PaM algorithm, it is possible to adaptively adjust the sensing frequency for pervasively monitoring various kinds of targets with random movement patterns. In addition, a prediction failure recovery mechanism called "mesh" is proposed to relocate the targets under tracking. Simulation results show that the PaM algorithm is robust against diverse motion changes and has excellent performance.
adaptive tracking wireless sensor network predict mesh tracking system energy saving