We describe a novel method for node localization in a sensor network where there are a fraction of reference nodes with known locations. For application-specific sensor networks, we argue that it makes sense to treat localization through online distributed learning and integrate it with an application task such as target tracking. We propose distributed online algorithm in which sensor nodes use geometric constraints induced by both radio connectivity and sensing to decrease the uncertainty of their position. The sensing constraints, which are caused by a commonly sensed moving target, are usually tighter than connectivity based constraints and lead to a decrease in average localization error over time. Different sensing models, such as radial binary detection and distance-bound estimation, are considered. First, we demonstrate our approach by studying a simple scenario in which a moving beacon broadcasts its own coordinates to the nodes in its vicinity. We then generalize this to the case when instead of a beacon, there is a moving target with a-priori unknown coordinates. The algorithms presented are fully distributed and assume only local information exchange between neighboring nodes. Our results indicate that the proposed method can be used to significantly enhance the accuracy in position estimation, even when the fraction of reference nodes is small. We compare the efficiency of the distributed algorithms to the case when node positions are estimated using centralized (convex) programming. Finally, simulations using the TinyOSNido platform are used to study the performance in more realistic scenarios.
Galstyan et al. REF treats localization through online distributed learning and integrates it with target tracking given a fraction of anchor nodes while not requiring the moving object to have a-prior knowledge about its own location.
Distributed online localization in sensor networks using a moving target
IPSN '04
Computer Science