Energy-Balanced Scheduling for Target Tracking in Wireless Sensor Networks

Abstract

A novel energy-balanced task-scheduling method is proposed that extends the lifespan of wireless sensor networks (WSNs) for collaborative target tracking using an unscented Kalman filter (UKF) algorithm. It is shown that the tracking accuracy is approximately proportional to the number of active sensor nodes participating in collaborative tracking. Excessive sensor nodes thus may be put to sleep mode to conserve energy provided there are a sufficient number of active sensor nodes. It is then shown that the lifespan of a WSN is dictated by the distribution of residue energy of sensor nodes. Specifically, we have shown that an energy-balanced WSN is likely to maximize its lifespan. As such, at each step of the tracking task, the head node must judiciously select active nodes from all sensors within the sensing range to minimize residue energy variations (energy balanced) while achieving desired tracking accuracy. This is formulated as a subset selection problem, which is shown to have a complexity that is NP-hard. Several energy-balanced scheduling for tracking (EBaST) heuristic algorithms are proposed to solve this problem with polynomial execution complexities. Extensive simulations have been conducted to compare EBaST against some state-of-the-art scheduling algorithms. It is observed that EBaST is more capable of significantly extending the WSNs lifespan than competing algorithms while delivering comparable or better tracking accuracy.