{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T15:45:42Z","timestamp":1765295142451,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,6,20]],"date-time":"2020-06-20T00:00:00Z","timestamp":1592611200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91846205"],"award-info":[{"award-number":["91846205"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The rapid development of indoor localization techniques such as Wi-Fi and RFID makes it possible to obtain users\u2019 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\u2019 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.<\/jats:p>","DOI":"10.3390\/info11060330","type":"journal-article","created":{"date-parts":[[2020,6,22]],"date-time":"2020-06-22T06:46:12Z","timestamp":1592808372000},"page":"330","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Discovering Influential Positions in RFID-Based Indoor Tracking Data"],"prefix":"10.3390","volume":"11","author":[{"given":"Ye","family":"Jin","sequence":"first","affiliation":[{"name":"School of Software, Shandong University, Jinan 250100, China"}]},{"given":"Lizhen","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Software, Shandong University, Jinan 250100, China"},{"name":"National Engineering Laboratory for E-Commerce Technologies, Jinan 250100, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4632","DOI":"10.1109\/JIOT.2018.2795893","article-title":"PSOTrack: A RFID-Based System for Random Moving Objects Tracking in Unconstrained Indoor Environment","volume":"5","author":"Li","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_2","first-page":"153","article-title":"Semantics and Modeling of Indoor Moving Objects","volume":"7","author":"Jin","year":"2012","journal-title":"Int. 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Syst."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/6\/330\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:41:13Z","timestamp":1760175673000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/6\/330"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,20]]},"references-count":28,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["info11060330"],"URL":"https:\/\/doi.org\/10.3390\/info11060330","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2020,6,20]]}}}