Tracking and prediction of mobility without physical distance measurements in sensor networks

Existing methods for detection and tracking of mobile nodes and objects via sensor networks depend on physical localization of sensor nodes. Consequently, they suffer from disadvantages associated with localization, which is often costly, error prone, and in some cases even infeasible. Tracking mobile nodes in a topological coordinate domain is proposed as an alternative to that in geographic coordinate domain. Topological coordinates (TCs), the basis of recent developments in Topology Preserving Maps (TPMs), are derived from hop distances from each node to a small subset of nodes. A technique is presented to reduce the distortion closer to the edges of principal component based TPMs, thereby enhancing its accuracy, which in turn facilitates accurate mobility tracking. Topological Coordinate based Tracking and Prediction (TCTP) algorithm is proposed for tracking and predicting the position of mobile nodes in the TC domain. Simulation results presented show that tracking and detecting performance of TCTP is competitive with geographic coordinate based algorithms despite the fact that it relies only on hop distances to a subset of nodes.