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Sensors 2017, 17(4), 864; doi:10.3390/s17040864

A Robust Crowdsourcing-Based Indoor Localization System

1,2
,
1,2,* , 3
and
1,2,*
1
Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
2
Key Laboratory for Geo-Environment Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and Geoinformation, Shenzhen University, Shenzhen 518060, China
3
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Antonio R. Jiménez
Received: 9 January 2017 / Revised: 31 March 2017 / Accepted: 11 April 2017 / Published: 14 April 2017
(This article belongs to the Special Issue Smartphone-based Pedestrian Localization and Navigation)
View Full-Text   |   Download PDF [564 KB, uploaded 14 April 2017]   |  

Abstract

WiFi fingerprinting-based indoor localization has been widely used due to its simplicity and can be implemented on the smartphones. The major drawback of WiFi fingerprinting is that the radio map construction is very labor-intensive and time-consuming. Another drawback of WiFi fingerprinting is the Received Signal Strength (RSS) variance problem, caused by environmental changes and device diversity. RSS variance severely degrades the localization accuracy. In this paper, we propose a robust crowdsourcing-based indoor localization system (RCILS). RCILS can automatically construct the radio map using crowdsourcing data collected by smartphones. RCILS abstracts the indoor map as the semantics graph in which the edges are the possible user paths and the vertexes are the location where users may take special activities. RCILS extracts the activity sequence contained in the trajectories by activity detection and pedestrian dead-reckoning. Based on the semantics graph and activity sequence, crowdsourcing trajectories can be located and a radio map is constructed based on the localization results. For the RSS variance problem, RCILS uses the trajectory fingerprint model for indoor localization. During online localization, RCILS obtains an RSS sequence and realizes localization by matching the RSS sequence with the radio map. To evaluate RCILS, we apply RCILS in an office building. Experiment results demonstrate the efficiency and robustness of RCILS. View Full-Text
Keywords: indoor localization; crowdsourcing; radio map; smartphone indoor localization; crowdsourcing; radio map; smartphone
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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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Zhou, B.; Li, Q.; Mao, Q.; Tu, W. A Robust Crowdsourcing-Based Indoor Localization System. Sensors 2017, 17, 864.

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