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Appl. Sci. 2017, 7(5), 467; doi:10.3390/app7050467

Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data

1
Communication Research Center, Harbin Institute of Technology, Harbin 150001, China
2
Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Chien-Hung Liu
Received: 29 March 2017 / Revised: 26 April 2017 / Accepted: 26 April 2017 / Published: 29 April 2017
(This article belongs to the Section Computer Science and Electrical Engineering)
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Abstract

Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, lots of different devices are used in crowdsourcing system and less RSS values are collected by each device. Therefore, the crowdsourced RSS values are more erroneous and can result in significant localization errors. In order to eliminate the signal strength variations across diverse devices, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. After obtaining the uniform RSS values, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. As a result, the negative effect of the erroneous measurements could be mitigated. Since the AP locations need to be known in G-SSL algorithm, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy. View Full-Text
Keywords: Indoor localization; crowdsourcing; received signal strength; graph-based semi-supervised learning; linear regression; compressed sensing Indoor localization; crowdsourcing; received signal strength; graph-based semi-supervised learning; linear regression; compressed sensing
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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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Zhang, L.; Valaee, S.; Xu, Y.; Ma, L.; Vedadi, F. Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data. Appl. Sci. 2017, 7, 467.

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