TeamSense: Energy-efficient autonomous mobile wireless sensor networks for object tracking

A typical surveillance application involves the use of limited sensing resources to monitor highly dynamic and uncertain environments. As a result, there is increasing demand to develop self-optimizing wireless sensor networks that are capable of adapting to such conditions. In this paper, we present a modeling framework, TeamSense, for designing and evaluating the performance of autonomous mobile wireless sensor networks for object-tracking applications. We program each sensor node to allow peer communication and collaboration to track several objects concurrently while reducing the network´s overall energy consumption. Operating in dynamic, decentralized teams, sensor nodes continuously fuse gathered data to enhance the estimation and prediction of the objects´ trajectories and optimally reconfigure the formed teams. The optimal reconfiguration of each team is obtained through solving a mixed-integer program. We perform a set of experiments to examine the performance of the developed network. Our results show that the TeamSense algorithms are computationally efficient and achieve a high level of tracking performance while using limited sensing resources and maintaining desirable energy consumption levels.