Document Type

Article

Publication Date

6-2017

DOI

10.3390/axioms6020015

Publication Title

Axioms

Volume

6

Issue

2

Pages

15 (24 pp.)

Abstract

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. © 2017 by the authors.

Comments

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.

(http://creativecommons.org/licenses/by/4.0/)

Original Publication Citation

Jin, M., Xia, S., Wu, H., & Gu, X. D. (2017). Scalable and fully distributed localization in large-scale sensor networks. Axioms, 6(2), 15. doi:10.3390/axioms6020015

ORCID

0000-0003-0897-8946 (Wu)

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