Abstract
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: Direct side models, where the RSSI is sensed by one antenna on the tag side, and obstruction side 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.
- 2012. IEEE standard for local and metropolitan area networks—Part 15.6: Wireless body area networks. IEEE Std 802.15.6-2012 (Feb. 2012), 1--271.Google Scholar
- Yijian Bai, Fusheng Wang, and Peiya Liu. 2006. Efficiently filtering RFID data streams. In Proceedings of the International VLDB Workshop on Clean Databases (Cleandb’06). 50--57.Google Scholar
- Gi Hwan Bong, Yoon Seok Chang, and Heun Oh Chang. 2014. A practical algorithm for reliability-based RFID event management considering warehouse operational environment. Int. J. Adv. Log. 3, 3 (2014), 100--108.Google ScholarCross Ref
- J. Brusey, Mg Harrison, and C. Floerkemeier. 2003. Reasoning about uncertainty in location identification with auto-ID. Automatic Identification Rfid Manufacturing (2003).Google Scholar
- Wang Chen, Wei Wei, Hongzhi Lin, Hongbo Jiang, and John C. S. Lui. 2016. BLOW-UP: Toward distributed and scalable space filling curve construction in 3D volumetric WSNs. ACM Trans. Sens. Netw. 12, 4 (2016), 30.Google Scholar
- Tsan Ming Choi, Wing Kwan Yeung, T. C. Edwin Cheng, and Xiaohang Yue. 2018. Optimal scheduling, coordination, and the value of RFID technology in garment manufacturing supply chains. IEEE Trans. Eng. Manage., 99 (2018), 1--13.Google Scholar
- Oscar Delgado-Mohatar, Amparo Fúster-Sabater, and José M. Sierra. 2011. A light-weight authentication scheme for wireless sensor networks. Ad Hoc Netw. 9, 5 (2011), 727--735.Google ScholarDigital Library
- Xuming Fang, Nan Lei, Zonghua Jiang, and Lijun Chen. 2017. Noise-aware fingerprint localization algorithm for wireless sensor network based on adaptive fingerprint Kalman filter. Comput. Netw. 124 (2017), 97--107.Google ScholarCross Ref
- T. Germa, F. Lerasle, N. Ouadah, and V. Cadenat. 2010. Vision and RFID data fusion for tracking people in crowds by a mobile robot. Comput. Vis. Image Understand. 114, 6 (2010), 641--651.Google ScholarDigital Library
- Alexia Giannoula, Alba Gutierrezsacristán, Álex Bravo, Ferran Sanz, and Laura I. Furlong. 2018. Identifying temporal patterns in patient disease trajectories using dynamic time warping: A population-based study. Sci. Rep. 8, 1 (2018), 4216.Google Scholar
- M. Goller, C. Feichtenhofer, and A. Pinz. 2014. Fusing RFID and Computer Vision for Probabilistic Tag Localization. 89--96 pages.Google Scholar
- Ding Han, Jinsong Han, Longfei Shangguan, Xi Wei, Zhiping Jiang, Yang Zheng, Zimu Zhou, Panglong Yang, and Ji Zhong Zhao. 2017. A platform for free-weight exercise monitoring with passive tags. IEEE Trans. Mobile Comput., 99 (2017), 1--1.Google Scholar
- J. [3]. Han and M. Kamber. 2005. Data Mining: Concepts and Technique.Google Scholar
- Matthias Hauser, Daniel Z¨gner, Christoph M. Flath, and Frédéric Thiesse. 2015. Pushing the limits of RFID: Empowering RFID-based electronic article surveillance with data analytics techniques. In Proceedings of the International Conference on Information Systems.Google Scholar
- Wei Qing Huang, Chang Ding, Si Ye Wang, Junyu Lin, Shao Yi Zhu, and Yue Cui. 2017. Design and realization of an indoor positioning algorithm based on differential positioning method. In Proceedings of the International Conference on Wireless Algorithms, Systems, and Applications. 546--558.Google ScholarCross Ref
- C. Jiang, Y. He, X. Zheng, and Y. Liu. 2018. Orientation-aware RFID tracking with centimeter-level accuracy. In Proceedings of the 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN’18). 290--301.Google Scholar
- Yu Ju Tu and Selwyn Piramuthu. 2008. Reducing false reads in RFID-embedded supply chains. J. Theor. Appl. Electr. Comm. Res. 3, 2 (2008), 60--70.Google ScholarDigital Library
- Thorben Keller and Elgar Fleisch. 2014. Classification Models for RFID-Based Real-Time Detection of Process Events in the Supply Chain: An Empirical Study. ACM.Google Scholar
- Thorben Keller, Frédéric Thiesse, and Elgar Fleisch. 2014. Using dynamic time warping to identify RFID tag movement in a logistics scenario with and without additional process knowledge. (2014).Google Scholar
- T. Keller, F. Thiesse, A. Ilic, and E. Fleisch. 2013. Decreasing false-positive RFID tag reads by improved portal antenna setups. In Internet of Things. 99--106.Google Scholar
- Thorben Keller, Frédéric Thiesse, Jens Kungl, and Elgar Fleisch. 2010. Using low-level reader data to detect false-positive RFID tag reads. In Internet of Things. 1--8.Google Scholar
- Yudai Komori, Kazuya Sakai, and Satoshi Fukumoto. 2018. Fast and secure tag authentication in large-scale RFID systems using skip graphs ąî. Comput. Commun. 116 (2018), 77--89.Google ScholarCross Ref
- LaurieDavies and UrsulaGather. 1993. The identification of multiple outliers. Publ. Am. Stat. Assoc. 88, 423 (1993), 782--792.Google ScholarCross Ref
- Laurie Davies and Ursula Gather. 1993. The identification of multiple outliers. J. Am. Stat. Assoc. 88, 423 (1993), 782--792.Google ScholarCross Ref
- Xie Lei, Chuyu Wang, Alex X. Liu, Jianqiang Sun, and Sanglu Lu. 2018. Multi-touch in the air: Concurrent micromovement recognition using RF signals. IEEE/ACM Trans. Netw., 99 (2018), 1--14.Google Scholar
- Tao Li, Shigang Chen, and Yibei Ling. 2013. Efficient protocols for identifying the missing tags in a large RFID system. IEEE/ACM Trans. Netw. 21, 6 (2013), 1974--1987.Google ScholarDigital Library
- T. Liu, L. Yang, Q. Lin, Y. Guo, and Y. Liu. 2014. Anchor-free backscatter positioning for RFID tags with high accuracy. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’14). 379--387.Google Scholar
- Haishu Ma, Yi Wang, and Kesheng Wang. 2018. Automatic detection of false positive RFID readings using machine learning algorithms. Expert Syst. Appl. 91 (2018).Google Scholar
- Martin Mayer and Norbert Goertz. 2016. RFID tag acquisition via compressed sensing: Fixed vs. random signature assignment. IEEE Trans. Wireless Commun. 15, 3 (2016), 2118--2129.Google ScholarDigital Library
- Lionel M. Ni, Yunhao Liu, Yiu Cho Lau, and Abhishek P. Patil. 2004. LANDMARC: Indoor location sensing using active RFID. 10, 6 (2004), 701--710.Google Scholar
- Saiyu Qi, Yuanqing Zheng, Mo Li, Yunhao Liu, and Jinli Qiu. 2016. Scalable industry data access control in RFID-enabled supply chain. IEEE/ACM Trans. Netw. 24, 6 (2016), 3551--3564.Google ScholarDigital Library
- L. Qiu, X. Liang, and Z. Huang. 2017. PATL: A RFID tag localization based on phased array antenna. Sci. Rep. 7 (2017), 44183.Google Scholar
- Mark Roberti. 2004. EPCglobal ratifies Gen 2 standard. RFID J. 16 (2004).Google Scholar
- A. P. Sample, C. Macomber, Liang Ting Jiang, and J. R. Smith. 2016. Optical localization of passive UHF RFID tags with integrated LEDs. In Proceedings of the IEEE International Conference on RFID.Google Scholar
- L. Shangguan, Z. Yang, A. X. Liu, Z. Zhou, and Y. Liu. 2017. STPP: Spatial-temporal phase profiling-based method for relative RFID tag localization. IEEE/ACM Trans. Netw. 25, 1 (2017), 596--609.Google ScholarDigital Library
- Rudranshu Sharma and Ankur Singh Bist. 2015. Machine learning: A survey. Int. J. Eng. Sci. Res. Technol. 4, 3 (2015).Google Scholar
- Chuyu Wang, Lei Xie, Wei Wang, Yingying Chen, Yanling Bu, and Sanglu Lu. 2018. RF-ECG: Heart rate variability assessment based on COTS RFID tag array. Proc. ACM Interact. Mobile Wear. Ubiq. Technol. 2, 07 (2018), 1--26. DOI:https://doi.org/10.1145/3214288Google ScholarDigital Library
- Gu Yu, Jinhai Zhan, Yusheng Ji, Li Jie, and Shangbing Gao. 2017. MoSense: A RF-based motion detection system via off-the-shelf WiFi devices. IEEE IofT J., 99 (2017), 1--1.Google Scholar
- 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.Google Scholar
- Dali Zhu, Bobai Zhao, and Siye Wang. 2018. Mobile target indoor tracking based on multi-direction weight position Kalman filter. Comput. Netw. 141 (2018), 115--127.Google ScholarCross Ref
- 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’18). 338--345.Google Scholar
Index Terms
- RF-AMOC: Human-related RFID Tag Movement Identification in Access Management of Carries
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