SCaNME: Location tracking system in large-scale campus Wi-Fi environment using unlabeled mobility map

Authors:
Mu Zhou;Zengshan Tian;Kunjie Xu;Xiang Yu;Xia Hong;Haibo Wu
Affiliations:
Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, China and Department of Electronic and Computer Engineering, The Hong Kong U ...;Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, China;Graduate Telecommunications and Networking Program, University of Pittsburgh, PA, USA;Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, China;Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, China;China Internet Research Lab, China Science and Technology Network, Computer Network Information Center, Chinese Academy of Sciences, Beijing, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2014

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Abstract

In this paper, we propose a novel location tracking system called SCaNME (Shotgun Clustering-aided Navigation in Mobile Environment) which iteratively sequences the clusters of sporadically recorded received signal strength (RSS) measurements and adaptively construct a mobility map of the environment for location tracking. In the SCaNME system, the location tracking problem is solved by first matching the people's locations to the location points (LPs) with small Kullback-Leibler (KL) divergence. Then, Allen's logics are applied to reveal the person's activities, assist the on-line location tracking and finally obtain a refined path estimate. The experimental results conducted on the large-scale HKUST campus demonstrate that the SCaNME tracking system provides better precision and reliability than the conventional location tracking systems. Furthermore, the experiments of SCaNME tracking system show its capability of providing people's real-time locations without fingerprint calibration in large-scale Wi-Fi environment.