Distributed localization for anisotropic sensor networks

In this article, we address the issue of localization in anisotropic sensor networks. Anisotropic networks differ 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 (nonconvex region), different node densities, irregular radio patterns, and anisotropic terrain conditions. In order to characterize anisotropic features, we devise a linear mapping method that projects one embedding space built upon proximity measures into geographic distance 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 noise 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.