Abstract

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 …

Date

April 26, 2004

Authors

Aram Galstyan, Bhaskar Krishnamachari, Kristina Lerman, Sundeep Pattem

Book

Proceedings of the 3rd international symposium on Information processing in sensor networks

Pages

61-70