Preprint Article Version 1 This version is not peer-reviewed

Graph-based Semi-supervised Learning for Indoor Localization Using Crowdsourced Data

Version 1 : Received: 18 April 2017 / Approved: 18 April 2017 / Online: 18 April 2017 (12:33:47 CEST)

A peer-reviewed article of this Preprint also exists.

Zhang, L.; Valaee, S.; Xu, Y.B.; Ma, L.; Vedadi, F. Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data. Appl. Sci. 2017, 7, 467. Zhang, L.; Valaee, S.; Xu, Y.B.; Ma, L.; Vedadi, F. Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data. Appl. Sci. 2017, 7, 467.

Journal reference: Appl. Sci. 2017, 7, 467
DOI: 10.3390/app7050467

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, crowdsourced RSS values are more erroneous and can result in large localization errors. To mitigate the negative effect of the erroneous measurements, 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. Before using the G-SSL method, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. Since the spatial distribution of the APs is sparse, 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.

Subject Areas

indoor localization; crowdsourcing; received signal strength; graph-based semi-supervised learning; linear regression; compressed sensing.

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