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• W2891336776 startingPage "2974" @default.
• W2891336776 abstract "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%." @default.
• W2891336776 created "2018-09-27" @default.
• W2891336776 creator A5001793981 @default.
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• W2891336776 date "2018-09-06" @default.
• W2891336776 modified "2023-10-17" @default.
• W2891336776 title "Wireless Sensor Network Localization via Matrix Completion Based on Bregman Divergence" @default.
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• W2891336776 doi "https://doi.org/10.3390/s18092974" @default.
• W2891336776 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6163867" @default.
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• W2891336776 hasPublicationYear "2018" @default.
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