Power-Aware Markov Chain Based Tracking Approach for Wireless Sensor Networks

We propose a novel measure method of information utility for tracking and localization in wireless sensor networks (WSNs). The target moving arbitrarily in WSNs is modeled by Markov chains using a transition matrix. The proposed information utility measurement allows us to expect the next state of the target and identify the informative sensors. Further, compared with existing localization methods, the proposed power-aware sensor selection considers the energy constraint of WSNs. To conserve energy, subsets of sensor nodes are activated based on a combinative measurement including information utility, communication cost, and residual energy. We have implemented the proposed localization system on real motes and experimented in an obstacle-free environment. The experimental results demonstrate that the proposed method outperforms two popular baseline schemes, k-nearest-neighbor and stochastic schemes, at extending the network lifetime. In addition, it balances the energy level of sensors in the network so that energy consumption is spread uniformly over all the sensors.