Energy-efficient adaptive sensor scheduling for target tracking in wireless sensor networks

Sensor scheduling is essential to collaborative target tracking in wireless sensor networks (WSNs). In theexisting works for target tracking in WSNs, such as the information-driven sensor query (IDSQ), the tasking sensors arescheduled to maximize the information gain while minimizing the resource cost based on the uniform sampling intervals,ignoring the changing of the target dynamics and the specific desirable tracking goals. This paper proposes a novel energyefficientadaptive sensor scheduling approach that jointly selects tasking sensors and determines their associated samplingintervals according to the predicted tracking accuracy and tracking energy cost. At each time step, the sensors are scheduledin alternative tracking mode, namely, the fast tracking mode with smallest sampling interval or the tracking maintenancemode with larger sampling interval, according to a specified tracking error threshold. The approach employs an extendedKalman filter (EKF)-based estimation technique to predict the tracking accuracy and adopts an energy consumption modelto predict the energy cost. Simulation results demonstrate that, compared to a non-adaptive sensor scheduling approach, theproposed approach can save energy cost significantly without degrading the tracking accuracy.