Metadata-Based Adaptive Sampling for Energy-Efficient Collaborative Target Tracking in Wireless Sensor Networks

The increasingly complex roles for which Wireless Sensor Networks (WSNs) are being employed have driven the desire for energy-efficient reliable target tracking. In this paper, a biologically inspired, adaptive energy-efficient multi-sensor scheme is proposed for collaborative target tracking in WSNs. Behavioural data gleaned whilst tracking the target is recorded as metadata to maintain the tracking accuracy. The group of tasking sensors that track the target is selected proactively according to the information associated with the predicted target location probability distribution. One of the selected tasking sensors is elected as a main node for management operations to improve the energy efficiency. Simulation results show that the developed adaptive multi-sensor scheme can achieve a significant reduction in energy consumption and seamless tracking compared with uniform sampling interval schemes.