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We apply sensor networks to the problem of tracking moving objects. We describe a publish-and-subscribe tracking method, called scalable tracking using networked sensors (STUN), that scales well to large numbers of sensors and moving objects by using hierarchy. We also describe a method, called drain-and-balance (DAB), for building efficient tracking hierarchies, computed from expected characteristics of the objects movement patterns. DAB is shown to perform well by running it on 1D and 2D sensor network topologies, and comparing it to schemes, which do not utilize movement information.