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Open AccessArticle

Wireless Sensor Network Localization via Matrix Completion Based on Bregman Divergence

Electronic Engineering Institute, National University of Defense Technology, Hefei 230037, China
Author to whom correspondence should be addressed.
Sensors 2018, 18(9), 2974;
Received: 17 July 2018 / Revised: 3 September 2018 / Accepted: 4 September 2018 / Published: 6 September 2018
(This article belongs to the Section Sensor Networks)
One of the main challenges faced by wireless sensor network (WSN) localization is the positioning accuracy of the WSN node. The existing algorithms are arduous to use for dealing with the pulse noise that is universal and ineluctable in practical considerations, resulting in lower positioning accuracy. Aimed at this problem and introducing Bregman divergence, we propose in this paper a novel WSN localization algorithm via matrix completion (LBDMC). Based on the natural low-rank character of the Euclidean Distance Matrix (EDM), the problem of EDM recovery is formulated as an issue of matrix completion in a noisy environment. A regularized matrix completion model is established, smoothing the pulse noise by leveraging L 1 , 2 -norm and the multivariate function Bregman divergence is defined to solve the model to obtain the EDM estimator. Furthermore, node localization is available based on the multi-dimensional scaling (MDS) method. Multi-faceted comparison experiments with existing algorithms, under a variety of noise conditions, demonstrate the superiority of LBDMC to other algorithms regarding positioning accuracy and robustness, while ensuring high efficiency. Notably, the mean localization error of LBDMC is about ten times smaller than that of other algorithms when the sampling rate reaches a certain level, such as >30%. View Full-Text
Keywords: localization; matrix completion; Bregman divergence; pulse noise localization; matrix completion; Bregman divergence; pulse noise
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Liu, C.; Shan, H.; Wang, B. Wireless Sensor Network Localization via Matrix Completion Based on Bregman Divergence. Sensors 2018, 18, 2974.

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