Tracking-Based Trajectory Data Reduction in Wireless Sensor Networks

This work addresses the problem of balancing the trade-off between the energy expenses due to communication vs. the accuracy of the trajectories representation in Wireless Sensor Networks (WSN) where the spatio-temporal data is obtained by tracking. We consider some of the approaches used by the Moving Objects Databases (MOD) and Computational Geometry (CG) communities, and we demonstrate that, with appropriate modifications, they can yield benefits in WSN in terms of energy savings. Towards that, we developed distributed algorithms that implement the Dead-Reckoning policy for managing the transient location-in-time information of mobile entities, whose localization is done by tracking sensors. In addition, we developed a distributed variant of the Douglas-Peuker heuristic for polyline reduction from CG literature, augmented with temporal awareness. Our experiments demonstrate the benefits in terms of reducing the communication overheads, while keeping the error boundaries at acceptable levels. Although it may seem counter-intuitive at first, we also demonstrate that an attempt to merge the Dead-Reckoning and Douglas-Peuker approaches, need not yield additional improvements of the in-network energy savings, due to the complementary nature of the data reduction in the two approaches.