SHARP: A New Approach to Relative Localization in Wireless Sensor Networks

Abstract

For wireless sensor networks, localization is crucial due to the dynamic nature of deployment. In relative localization, nodes use the distance measurements to estimate their positions relative to some coordinate system. In absolute localization, a few nodes (called anchors) need to know their absolute positions, and all the other nodes are absolutely localized in the coordinate system of the anchors. Relative and absolute localization methods differ in both the performance and the cost. We present a new approach to relative localization that we refer to as: simple hybrid absolute-relative positioning (SHARP). In SHARP, a relative localization method (M1) is used to relatively localize N/sub r/ reference nodes. Then, an absolute localization method (M2) uses these N/sub r/ nodes as anchors to localize the rest of the nodes. Choosing N/sub r/, M1, and M2 gives a wide range of performance-cost tuning. We have done extensive simulation using the multidimensional scaling (MDS) method as M1 and the ad-hoc positioning system (APS) method as M2. While previous research shows that MDS gives better localization results than APS, our simulation shows that SHARP outperforms MDS if both the localization error and the cost are considered.