Localization in Sensor Networks with Limited Number of Anchors and Clustered Placement

Abstract

Many localization algorithms have been proposed in recent years. Although different algorithms based on different methodologies, the use of anchors is common to most algorithms. The placement and the density of anchors affect the accuracy of different algorithms to different extent. Location estimates are usually more accurate with a higher density of anchors. When there are only a few anchors, efficient algorithms tend to perform poorly. However, having more anchors will increase the cost of a sensor network. In this paper, we present an algorithm which uses two different localization techniques, multidimensional scaling (MDS) and proximity-distance map (PDM), in a phased approach. MDS has a high complexity but can give good results when there are only very few anchors. PDM, on the other hand, is a distributed algorithm but performs poorly when anchors are scarce. The phased approach has comparable complexity to PDM but less than MDS. With extensive simulations, we demonstrate that the proposed algorithm gives accurate solution with very few anchors or clustered anchors which is intrinsically a difficult challenge to most existing algorithms.