Sensor scheduling for energy-efficient target tracking in sensor networks

In this paper we study the problem of tracking an object moving randomly through a network of wireless sensors. Our objective is to devise strategies for scheduling the sensors to optimize the tradeoff between tracking performance and energy consumption. We cast the scheduling problem as a Partially Observable Markov Decision Process (POMDP) where the control actions correspond to the set of sensors to activate at each time step. Using a bottom-up approach, we consider different sensing, motion and cost models with increasing level of difficulty. At the first level, the sensing regions of the different sensors do not overlap and the target is only observed within the sensing range of an active sensor. Then, we consider sensors with overlapping sensing range such that the tracking error, and hence actions for different sensors, are tightly coupled. Finally, we consider scenarios wherein the sensors' observations assume values on a continuous space. An exact solution is generally intractable even for the simplest model due to the dimensionality of the information and action spaces. Hence, we devise approximate solution techniques and in some cases derive lower bounds on the optimal tradeoff. The generated scheduling policies, albeit suboptimal, often provide close-to-optimal energy-tracking tradeoffs.