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Open AccessArticle

Discovering Influential Positions in RFID-Based Indoor Tracking Data

by Ye Jin 1 and Lizhen Cui 1,2,*
School of Software, Shandong University, Jinan 250100, China
National Engineering Laboratory for E-Commerce Technologies, Jinan 250100, China
Author to whom correspondence should be addressed.
Information 2020, 11(6), 330;
Received: 17 May 2020 / Revised: 15 June 2020 / Accepted: 16 June 2020 / Published: 20 June 2020
(This article belongs to the Special Issue Indoor Navigation in Smart Cities)
The rapid development of indoor localization techniques such as Wi-Fi and RFID makes it possible to obtain users’ position-tracking data in indoor space. Indoor position-tracking data, also known as indoor moving trajectories, offer many new opportunities to mine decision-making knowledge. In this paper, we study the detection of highly influential positions from indoor position-tracking data, e.g., to detect highly influential positions in a business center, or to detect the hottest shops in a shopping mall according to users’ indoor position-tracking data. We first describe three baseline solutions to this problem, which are count-based, density-based, and duration-based algorithms. Then, motivated by the H-index for evaluating the influence of an author or a journal in academia, we propose a new algorithm called H-Count, which evaluates the influence of an indoor position similarly to the H-index. We further present an improvement of the H-Count by taking a filtering step to remove unqualified position-tracking records. This is based on the observation that many visits to a position such as a gate are meaningless for the detection of influential indoor positions. Finally, we simulate 100 moving objects in a real building deployed with 94 RFID readers over 30 days to generate 223,564 indoor moving trajectories, and conduct experiments to compare our proposed H-Count and H-Count* with three baseline algorithms. The results show that H-Count outperforms all baselines and H-Count* can further improve the F-measure of the H-Count by 113% on average. View Full-Text
Keywords: RFID; indoor space; indoor position-tracking data; indoor moving trajectory; influential position; H-count RFID; indoor space; indoor position-tracking data; indoor moving trajectory; influential position; H-count
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Jin, Y.; Cui, L. Discovering Influential Positions in RFID-Based Indoor Tracking Data. Information 2020, 11, 330.

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