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        <identifier>oai:drops-oai.dagstuhl.de:1114</identifier>
        <datestamp>2024-03-06T11:07:28Z</datestamp>
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          <dc:title>Discovery of Sensor Network Layout using Connectivity Information</dc:title>
          <dc:creator>Gao, Jie</dc:creator>
          <dc:creator>Lederer, Sol</dc:creator>
          <dc:creator>Wang, Yue</dc:creator>
          <dc:subject>Sensor Networks</dc:subject>
          <dc:subject>Localization</dc:subject>
          <dc:subject>Delaunay complex</dc:subject>
          <dc:subject>Rigidity</dc:subject>
          <dc:description>We propose a distributed algorithm to discover and recover the&#13;
layout of a large sensor network having a complex shape. As sensor&#13;
network deployments grow large in size and become non-uniform,&#13;
localization algorithms suffer from ``flip'' ambiguities---where a&#13;
part of the network folds on top of another while keeping all edge&#13;
length measurements preserved. We explore the high-order topological&#13;
information in a sensor field to prevent incorrect flips and&#13;
accurately recover the shape of the sensor network. We select&#13;
landmarks on network boundaries with sufficient density, construct&#13;
the landmark Voronoi diagram and its dual combinatorial Delaunay&#13;
complex on these landmarks. The key insight is that when the&#13;
landmarks are dense enough to capture the local geometric&#13;
complexity, the combinatorial Delaunay complex is globally rigid and&#13;
has a unique realization in the plane. An embedding by simply gluing&#13;
the Delaunay triangles properly derives a faithful network layout,&#13;
which consequently leads to a practical and sufficiently accurate&#13;
localization algorithm. We prove the global rigidity of the&#13;
combinatorial Delaunay complex in the case of a continuous geometric&#13;
region. Simulation results on discrete networks show surprisingly&#13;
good results, while multi-dimensional scaling and rubberband&#13;
representation perform poorly or not at all in recovering the&#13;
network layout.&#13;
&#13;
This is joint work with Sol Lederer and Yue Wang.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Jie Gao and Sol Lederer and Yue Wang</dc:contributor>
          <dc:date>2007</dc:date>
          <dc:relation>Is Part Of Dagstuhl Seminar Proceedings, Volume 7151, Geometry in Sensor Networks (2007)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
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          <dc:format>application/pdf</dc:format>
          <dc:identifier>doi:10.4230/DagSemProc.07151.2</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-11149</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07151.2</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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