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Given a complex geospatial network with nodes distributed in a two-dimensional region of physical space, can the locations of the nodes be determined and their connection patterns be uncovered based solely on data? We consider the realistic situation where time

Given a complex geospatial network with nodes distributed in a two-dimensional region of physical space, can the locations of the nodes be determined and their connection patterns be uncovered based solely on data? We consider the realistic situation where time series/signals can be collected from a single location. A key challenge is that the signals collected are necessarily time delayed, due to the varying physical distances from the nodes to the data collection centre. To meet this challenge, we develop a compressive-sensing-based approach enabling reconstruction of the full topology of the underlying geospatial network and more importantly, accurate estimate of the time delays. A standard triangularization algorithm can then be employed to find the physical locations of the nodes in the network. We further demonstrate successful detection of a hidden node (or a hidden source or threat), from which no signal can be obtained, through accurate detection of all its neighbouring nodes. As a geospatial network has the feature that a node tends to connect with geophysically nearby nodes, the localized region that contains the hidden node can be identified.

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    Title
    • Data-Based Reconstruction of Complex Geospatial Networks, Nodal Positioning, and Detection of Hidden Nodes
    Contributors
    Date Created
    2016-01-06
    Resource Type
  • Text
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    Identifier
    • Digital object identifier: 10.1098/rsos.150577
    • Identifier Type
      International standard serial number
      Identifier Value
      2054-5703
    Note
    • The final version of this article, as published in Royal Society Open Science, can be viewed online at: http://rsos.royalsocietypublishing.org/content/3/1/150577

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    This is a suggested citation. Consult the appropriate style guide for specific citation guidelines.

    Su, R., Wang, W., Wang, X., & Lai, Y. (2016). Data-based reconstruction of complex geospatial networks, nodal positioning and detection of hidden nodes. Royal Society Open Science, 3(1), 150577. doi:10.1098/rsos.150577

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