Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151323
Title: Learning-based outdoor localization exploiting crowd-labeled WiFi hotspots
Authors: Wang, Jin
Luo, Jun
Pan, Sinno Jialin
Sun, Aixin
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Wang, J., Luo, J., Pan, S. J. & Sun, A. (2018). Learning-based outdoor localization exploiting crowd-labeled WiFi hotspots. IEEE Transactions On Mobile Computing, 18(4), 896-909. https://dx.doi.org/10.1109/TMC.2018.2849416
Project: MOE2016-T2-2-022
Journal: IEEE Transactions on Mobile Computing
Abstract: The ever-expanding scale of WiFi deployments in metropolitan areas has made accurate GPS-free outdoor localization possible by relying solely on the WiFi infrastructure. Nevertheless, neither academic researches nor existing industrial practices seem to provide a satisfactory solution or implementation. In this paper, we propose WOLoc (WiFi-only Outdoor Localization) as a learning-based outdoor localization solution using only WiFi hotspots labeled by crowdsensing. On one hand, we do not take these labels as fingerprints as it is almost impossible to extend indoor localization mechanisms by fingerprinting metropolitan areas. On the other hand, we avoid the over-simplified local synthesis methods (e.g., centroid) that significantly lose the information contained in the labels. Instead, WOLoc adopts a semi-supervised manifold learning approach that accommodates all the labeled and unlabeled data for a given area, and the output concerning the unlabeled part will become the estimated locations for both unknown users and unknown WiFi hotspots. Moreover, WOLoc applies text mining techniques to analyze the SSIDs of hotspots, so as to derive more accurate input to its manifold learning. We conduct extensive experiments in several outdoor areas, and the results have strongly indicated the efficacy of our solution in achieving a meter-level localization accuracy.
URI: https://hdl.handle.net/10356/151323
ISSN: 1536-1233
DOI: 10.1109/TMC.2018.2849416
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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