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Netw."],"published-print":{"date-parts":[[2020,11,30]]},"abstract":"<jats:p>\n            The use of radio-frequency identification (RFID) technology in supply chain has been a fairly mature application in recent years, which can be extended to the field of carrier management for the inventory and access control of sensitive files and mobile storage medium. To address the inherent defects of false readings of RFID, we present RF-AMOC, a tag movement identification system that leverages the signal variation patterns between the opposite antennas and the tag to accurately determine whether someone takes the sensitive carrier out of the room or just the normal carrier usage activity in the room. Particularly, we focus on two kinds of signal variation modes:\n            <jats:italic>Direct side<\/jats:italic>\n            models, where the RSSI is sensed by one antenna on the tag side, and\n            <jats:italic>obstruction side<\/jats:italic>\n            models, where the RSSI is sensed by the other antenna that was obstructed by the person. Then, Pearson Coefficient and crest comparison algorithms are adopted to match the theoretical and actual RF-signal curves on the two sides, respectively. Additionally, a starting point acquisition method is proposed to extract the meaningful time period. A prototype of RF-AMOC is realized in two different environments with various persons, and the results validate that it is superior in terms of sensitivity and specificity with strong robustness.\n          <\/jats:p>","DOI":"10.1145\/3399678","type":"journal-article","created":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T22:04:36Z","timestamp":1598393076000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["RF-AMOC"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2230-1210","authenticated-orcid":false,"given":"Shaoyi","family":"Zhu","sequence":"first","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences 8 School of Cyber Security, University of Chinese Academy of Sciences, Xingshikou Road, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiqing","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University 8 Institute of Information Engineering, Chinese Academy of Sciences 8 School of Cyber Security, University of Chinese Academy of Sciences, Xingshikou Road, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenggang","family":"Jia","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences 8 School of Cyber Security, University of Chinese Academy of Sciences, Xingshikou Road, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siye","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University 8 Institute of Information Engineering, Chinese Academy of Sciences 8 School of Cyber Security, University of Chinese Academy of Sciences, Xingshikou Road, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bowen","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences 8 School of Cyber Security, University of Chinese Academy of Sciences, Xingshikou Road, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanfang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences 8 School of Cyber Security, University of Chinese Academy of Sciences, Xingshikou Road, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,8,25]]},"reference":[{"volume-title":"IEEE standard for local and metropolitan area networks\u2014Part 15.6: Wireless body area networks","year":"2012","key":"e_1_2_1_1_1","unstructured":"2012. 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Computer vision-assisted region-of-interest RFID tag recognition and localization in multipath-prevalent environments . Proc. ACM Interact. Mobile Wear. Ubiq. Technol. 3 , 29 (2019), 1 -- 30 . Wang Zhongqin, Xu Min, Ye Ning, Wang Ruchuan, and Huang Haiping. 2019. Computer vision-assisted region-of-interest RFID tag recognition and localization in multipath-prevalent environments. Proc. ACM Interact. Mobile Wear. Ubiq. Technol. 3, 29 (2019), 1--30.","journal-title":"Proc. ACM Interact. Mobile Wear. Ubiq. Technol."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2018.05.021"},{"key":"e_1_2_1_41_1","unstructured":"Shaoyi Zhu Siye Wang Fangtao Zhang Yanfang Zhang Yue Feng and Weiqing Huang. 2018. Environmentally adaptive real-time detection of RFID false readings in a new practical scenario. In Proceedings of the 2018 IEEE SmartWorld Ubiquitous Intelligence 8 Computing Advanced 8 Trusted Computing Scalable Computing 8 Communications Cloud 8 Big Data Computing Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/CBDCom\/IOP\/SCI\u201918). 338--345.  Shaoyi Zhu Siye Wang Fangtao Zhang Yanfang Zhang Yue Feng and Weiqing Huang. 2018. Environmentally adaptive real-time detection of RFID false readings in a new practical scenario. 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