Improving tracking quality and extending network lifespan are two main objects for target tracking, which are usually contradictory due to limited energy of sensor nodes in wireless sensor networks. This article incorporates this contradiction into a problem of multi-objective optimization in tracking networks where multiple sensor nodes are scheduled for collaborative target tracking by adopting the unscented Kalman filter algorithm. We propose an effective scheme to extend the lifespan of wireless sensor networks while guaranteeing preset tracking quality. More specifically, with regard to practical circumstances, we perform analysis on the target detection probability, as well as residual energy of sensor nodes, when selecting a suitable set of candidate sensor nodes. Then, we put forward a novel energy-balanced sensor nodes scheduling algorithm, Greedy Balance Replace Heuristic Algorithm, to select a near-optimal task sensor set from the candidate sensor node set to balance tracking quality and network lifetime. In addition, we also design an efficient multi-sensor node collaborative method to track a single target and to timely report its state to the remote end. From simulation results, it is demonstrated that the proposed node scheduling scheme can not only maintain the preset tracking accuracy but also extend network lifespan with a low computation complexity.
Tracking a mobile target, which aims to timely monitor the invasion of specific target, is one of the most prominent applications in wireless sensor networks (WSNs). Traditional tracking methods in WSNs only based on static sensor nodes (SNs) have several critical problems. For example, to void the loss of mobile target, many SNs must be active to track the target in all possible directions, resulting in excessive energy consumption. Additionally, when entering coverage holes in the monitoring area, the mobile target may be missing and then its state is unknown during this period. To tackle these problems, in this paper, a few mobile sensor nodes (MNs) are introduced to cooperate with SNs to form a hybrid WSN due to their stronger abilities and less constrained energy. Then, we propose a valid target tracking scheme for hybrid WSNs to dynamically schedule the MNs and SNs. Moreover, a novel loss recovery mechanism is proposed to find the lost target and recover the tracking with fewer SNs awakened. Furthermore, to improve the robustness and accuracy of the recovery mechanism, an adaptive unscented Kalman filter (AUKF) algorithm is raised to dynamically adjust the process noise covariance. Simulation results demonstrate that our tracking scheme for maneuvering target in hybrid WSNs can not only track the target effectively even if the target is lost but also maintain an excellent accuracy and robustness with fewer activated nodes.
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