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Sensors 2018, 18(9), 2990;

Indoor Localization Based on Weighted Surfacing from Crowdsourced Samples

School of Electronic Information and Communications, Huazhong University of Science and Technology (HUST), Wuhan 430074, China
Chongqing Key Lab of Mobile Communications Technology, School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Author to whom correspondence should be addressed.
Received: 9 August 2018 / Revised: 28 August 2018 / Accepted: 3 September 2018 / Published: 7 September 2018
(This article belongs to the Special Issue Pervasive Intelligence and Computing)
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Fingerprinting-based indoor localization suffers from its time-consuming and labor-intensive site survey. As a promising solution, sample crowdsourcing has been recently promoted to exploit casually collected samples for building offline fingerprint database. However, crowdsourced samples may be annotated with erroneous locations, which raises a serious question about whether they are reliable for database construction. In this paper, we propose a cross-domain cluster intersection algorithm to weight each sample reliability. We then select those samples with higher weight to construct radio propagation surfaces by fitting polynomial functions. Furthermore, we employ an entropy-like measure to weight constructed surfaces for quantifying their different subarea consistencies and location discriminations in online positioning. Field measurements and experiments show that the proposed scheme can achieve high localization accuracy by well dealing with the sample annotation error and nonuniform density challenges. View Full-Text
Keywords: fingerprinting localization; sample crowdsourcing; sample weighting; surface fitting fingerprinting localization; sample crowdsourcing; sample weighting; surface fitting

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Lin, J.; Wang, B.; Yang, G.; Zhou, M. Indoor Localization Based on Weighted Surfacing from Crowdsourced Samples. Sensors 2018, 18, 2990.

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