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Scalable and Fully Distributed Localization in Large-Scale Sensor Networks

The Center for Advanced Computer Studies, University of Louisiana, Lafayette, LA 70504, USA
Cisco Systems, Milpitas, CA 95035, USA
The Center for Cybersecurity, Old Dominion University, Norfolk, VA 23529, USA
Department of Computer Science, Stony Brook University, Stony Brook, NY 11790, USA
Author to whom correspondence should be addressed.
Academic Editors: Emil Saucan and Hans Haubold
Axioms 2017, 6(2), 15;
Received: 30 March 2017 / Revised: 23 May 2017 / Accepted: 8 June 2017 / Published: 15 June 2017
(This article belongs to the Special Issue Discrete Geometry and its Applications)
PDF [46152 KB, uploaded 15 June 2017]


This work proposes a novel connectivity-based localization algorithm, well suitable for large-scale sensor networks with complex shapes and a non-uniform nodal distribution. In contrast to current state-of-the-art connectivity-based localization methods, the proposed algorithm is highly scalable with linear computation and communication costs with respect to the size of the network; and fully distributed where each node only needs the information of its neighbors without cumbersome partitioning and merging process. The algorithm is theoretically guaranteed and numerically stable. Moreover, the algorithm can be readily extended to the localization of networks with a one-hop transmission range distance measurement, and the propagation of the measurement error at one sensor node is limited within a small area of the network around the node. Extensive simulations and comparison with other methods under various representative network settings are carried out, showing the superior performance of the proposed algorithm. View Full-Text
Keywords: localization; large-scale sensor network; scalable; fully distributed localization; large-scale sensor network; scalable; fully distributed

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Jin, M.; Xia, S.; Wu, H.; Gu, X.D. Scalable and Fully Distributed Localization in Large-Scale Sensor Networks. Axioms 2017, 6, 15.

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