Next Article in Journal
How Managers Use Information Systems for Strategy Implementation in Agritourism SMEs
Previous Article in Journal / Special Issue
Classroom Attendance Systems Based on Bluetooth Low Energy Indoor Positioning Technology for Smart Campus
Open AccessArticle

Discovering Influential Positions in RFID-Based Indoor Tracking Data

by Ye Jin 1 and Lizhen Cui 1,2,*
1
School of Software, Shandong University, Jinan 250100, China
2
National Engineering Laboratory for E-Commerce Technologies, Jinan 250100, China
*
Author to whom correspondence should be addressed.
Information 2020, 11(6), 330; https://doi.org/10.3390/info11060330
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
Show Figures

Figure 1

MDPI and ACS Style

Jin, Y.; Cui, L. Discovering Influential Positions in RFID-Based Indoor Tracking Data. Information 2020, 11, 330.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop