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Theory of semidefinite programming for Sensor Network Localization

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Abstract

We analyze the semidefinite programming (SDP) based model and method for the position estimation problem in sensor network localization and other Euclidean distance geometry applications. We use SDP duality and interior-point algorithm theories to prove that the SDP localizes any network or graph that has unique sensor positions to fit given distance measures. Therefore, we show, for the first time, that these networks can be localized in polynomial time. We also give a simple and efficient criterion for checking whether a given instance of the localization problem has a unique realization in \(\mathcal{R}^2\) using graph rigidity theory. Finally, we introduce a notion called strong localizability and show that the SDP model will identify all strongly localizable sub-networks in the input network.

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Authors and Affiliations

  1. Department of Computer Science, Stanford University, Stanford, CA, 94305, USA

    Anthony Man-Cho So

  2. Department of Management Science and Engineering and, by courtesy, Electrical Engineering, Stanford University, Stanford, CA, 94305, USA

    Yinyu Ye

Authors
  1. Anthony Man-Cho So
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  2. Yinyu Ye
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Correspondence to Yinyu Ye.

Additional information

A preliminary version of this paper has appeared in the Proceedings of the 16th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2005.

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So, A.MC., Ye, Y. Theory of semidefinite programming for Sensor Network Localization. Math. Program. 109, 367–384 (2007). https://doi.org/10.1007/s10107-006-0040-1

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  • DOI: https://doi.org/10.1007/s10107-006-0040-1

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