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 stateof-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.

Efficient Node and Sensed Module Management for Multisensory Wireless Sensor Networks

“…The object is to obtain the minimum difference between the observation and actual value so as to get the optimal observation. In [9], the authors propose a novel energy-balanced task-scheduling method for collaborative target tracking using an unscented Kalman filter. At each step of the tracking task, the head node selects active nodes from all nodes within the sensing range to minimize residual energy variations.…”

“…To solve the node selection problem, the distance-based methods (such as [2,3,4]) are proposed, and they need less computation but cannot reach competitive tracking accuracy. To improve the tracking accuracy, the entropy-based methods (such as [5,6,7]) and the optimal theory-based methods (such as [8,9,10]) are proposed. Although they achieve good tracking accuracy, these methods are computationally expensive.…”

Is one-way barrier coverage achievable using comprehensive sensors?

“…Target tracking has long been an active topic in sensor network research (e.g. [27][28][29][30][31][32]). How to track one target with binary proximity sensors has been studied in [27].…”

“…In [31], Jiang et al designed a new protocol that can predict the target motion and select the sensors along the trajectory to awaken for target tracking with the remaining sensors sleeping, so as to enhance energy efficiency. Hu et al [32] pointed out that the tracking accuracy is approximately proportional to the number of active sensor nodes participating in collaborative tracking and designed several algorithms that select active nodes from all sensors while achieving desired tracking accuracy. Target tracking addresses on how to track the whole trajectories of targets.…”

An Effective and Robust Decentralized Target Tracking Scheme in Wireless Camera Sensor Networks

“…In target tracking, the current presence of moving targets will be detected by sampling the sensed signals (e.g., light, sound, image, or video) [4]. In recent years, with the price of smart camera dropping rapidly, the development of wireless camera sensor networks (WCNs) has been heavily fostered [5,6].…”