Opportunistic Processing and Query of Motion Trajectories in Wireless Sensor Networks

We study the problem of in-network processing and queries of trajectories of moving targets in a sensor network. The main idea is to exploit the spatial coherence of target trajectories for opportunistic information dissemination with no or small extra communication cost, as well as for efficient probabilistic queries searching for a given target signature in a real-time manner. Sensors near a moving target are waken up to record information about this target and take the communication opportunities to exchange their knowledge with preceding and descending sensor nodes along the trajectory. Thus a moving target's information is naturally detected, recorded, and disseminated along its trajectory, as well as the motion trajectories that enter the sensor field afterwards. We analyzed and through simulations tested the dissemination cost and query success rate for randomly generated data sets. Trajectories of reasonable length can be discovered by probabilistic in-network queries with high probability. Compared with the scheme without opportunistic dissemination, the in-network processing of trajectories, with modest cost on dissemination, allows substantially reduced query cost and delay.