In this paper, we address the problem of tracking cooperative mobile nodes in wireless sensor networks. Aiming at a resource efficient solution, we advocate the use of sensors that maintain their location information and rely on the tracking service only when their locations change. In the proposed approach, the tracked node transmits a signal and infrastructure nodes measure the Doppler shifts of the transmitted signal. We show that Mica2 motes can measure RF Doppler shifts with 0.2 Hz accuracy corresponding to a 0.14 m/s error in relative speed estimates using radio interferometric technique. The tracking problem is modeled as a non-linear optimization problem and an extended Kalman filter is used to solve it accurately assuming Gaussian measurement errors. However, this approach may fail if the tracked node changes its speed or direction. We propose to update the Kalman filter state by performing constrained least-squares optimization when a maneuver is detected. The combined approach achieves almost a 50 % accuracy improvement over the Kalman filter alone when the mobile node changes its direction and speed frequently. We describe our proof-ofconcept implementation of the tracking service and evaluate its performance experimentally and in simulation.

So-called sensor nodes combine means for sensing environmental parameters, processors, wireless communication capabilities, and autonomous power supply in a single compact device. Networks of these untethered devices can be deployed unobtrusively in the physical environment in order to monitor a wide variety of real-world phenomena with unprecedented quality and scale while only marginally disturbing the observed physical processes. Due to the close