Distributed Localization for Anisotropic Sensor Networks

In this paper, we address the issue of localization in anisotropic sensor networks. Anisotropic networks are differentiated from isotropic networks in that they possess properties that vary according to the direction of measurement. Anisotropic characteristics result from various factors such as the geographic shape of the region (non-convex region), the different node densities, the irregular radio patterns, and the anisotropic terrain conditions. In order to characterize anisotropic features, we devise a linear mapping method that transforms proximity measurements between sensor nodes into a geographic distance embedding space by using the truncated singular value decomposition (SVD) pseudo-inverse technique. This transformation retains as much topological information as possible and reduces the effect of measurement noises on the estimates of geographic distances. We show via simulation that the proposed localization method outperforms DV-hop, DV-distance, and MDS-map, and makes robust and accurate estimates of sensor locations in both isotropic and anisotropic sensor networks