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Sensors 2016, 16(3), 381; doi:10.3390/s16030381

Scalable Indoor Localization via Mobile Crowdsourcing and Gaussian Process

College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China
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Author to whom correspondence should be addressed.
Academic Editors: Lyudmila Mihaylova and Byung-Gyu Kim
Received: 21 January 2016 / Revised: 1 March 2016 / Accepted: 14 March 2016 / Published: 16 March 2016
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
View Full-Text   |   Download PDF [1958 KB, uploaded 16 March 2016]   |  

Abstract

Indoor localization using Received Signal Strength Indication (RSSI) fingerprinting has been extensively studied for decades. The positioning accuracy is highly dependent on the density of the signal database. In areas without calibration data, however, this algorithm breaks down. Building and updating a dense signal database is labor intensive, expensive, and even impossible in some areas. Researchers are continually searching for better algorithms to create and update dense databases more efficiently. In this paper, we propose a scalable indoor positioning algorithm that works both in surveyed and unsurveyed areas. We first propose Minimum Inverse Distance (MID) algorithm to build a virtual database with uniformly distributed virtual Reference Points (RP). The area covered by the virtual RPs can be larger than the surveyed area. A Local Gaussian Process (LGP) is then applied to estimate the virtual RPs’ RSSI values based on the crowdsourced training data. Finally, we improve the Bayesian algorithm to estimate the user’s location using the virtual database. All the parameters are optimized by simulations, and the new algorithm is tested on real-case scenarios. The results show that the new algorithm improves the accuracy by 25.5% in the surveyed area, with an average positioning error below 2.2 m for 80% of the cases. Moreover, the proposed algorithm can localize the users in the neighboring unsurveyed area. View Full-Text
Keywords: WLAN; indoor localization; radio map; mobile crowdsourcing; gaussian process; Bayesian algorithm WLAN; indoor localization; radio map; mobile crowdsourcing; gaussian process; Bayesian algorithm
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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MDPI and ACS Style

Chang, Q.; Li, Q.; Shi, Z.; Chen, W.; Wang, W. Scalable Indoor Localization via Mobile Crowdsourcing and Gaussian Process. Sensors 2016, 16, 381.

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