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Title:Distributed Localization for Anisotropic Sensor Networks
Author(s):Lim, Hyuk; Hou, Jennifer C.
Subject(s):sensor networks
Abstract: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.
Issue Date:2005-08
Genre:Technical Report
Type:Text
URI:http://hdl.handle.net/2142/11090
Other Identifier(s):UIUCDCS-R-2005-2628
Rights Information:You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s).
Date Available in IDEALS:2009-04-20


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