Page 442 - 《软件学报》2026年第1期

黄宇红 等: 基于 RFID 的无源物联网无线感知研究现状与发展趋势 439

passive tags. IEEE/ACM Trans. on Networking, 2016, 24(5): 2885–2898. [doi: 10.1109/TNET.2015.2501103]
[39] Zhou ZM, Shangguan LF, Zheng XL, Yang L, Liu YH. Design and implementation of an RFID-based customer shopping behavior
mining system. IEEE/ACM Trans. on Networking, 2017, 25(4): 2405–2418. [doi: 10.1109/TNET.2017.2689063]
[40] Wan CY, Tanriover C, Shah RC. Capturing customer browsing insights through RFID tag motion detection in high tag density
environments. In: Proc. of the 2020 IEEE Int’l Conf. on RFID. Orlando: IEEE, 2020. 1–8. [doi: 10.1109/RFID49298.2020.9244868]
[41] Bu YL, Xie L, Gong YY, Liu J, He BB, Cao JN, Ye BL, Lu SL. RF-3DScan: RFID-based 3D reconstruction on tagged packages. IEEE
Trans. on Mobile Computing, 2021, 20(2): 722–738. [doi: 10.1109/TMC.2019.2943853]
[42] Wang CY, Xie L, Wu JY, Zhang KY, Wang W, Bu YL, Lu SL. Spin-Antenna: Enhanced 3D motion tracking via spinning antenna based
on COTS RFID. IEEE Trans. on Mobile Computing, 2024, 23(2): 1347–1365. [doi: 10.1109/TMC.2023.3236360]
[43] Liu HC, Meng ZZ, Xu JR, Li CX, Li Z, Gao N, Zhang ZH. Simultaneous detection of the orientation and position of moving objects with
simple RFID array for industrial IoT applications. IEEE Internet of Things Journal, 2024, 11(17): 28752–28764. [doi: 10.1109/JIOT.2024.
3403195]
[44] Yang L, Li Y, Lin QZ, Jia HY, Li XY, Liu YH. Tagbeat: Sensing mechanical vibration period with COTS RFID systems. IEEE/ACM
Trans. on Networking, 2017, 25(6): 3823–3835. [doi: 10.1109/TNET.2017.2769138]
[45] Duan CH, Yang L, Jia HY, Lin QZ, Liu YH, Xie L. Robust spinning sensing with dual-RFID-tags in noisy settings. In: Proc. of the 2018
IEEE Conf. on Computer Communications. Honolulu: IEEE, 2018. 855–863. [doi: 10.1109/INFOCOM.2018.8486312]
[46] He Y, Zheng YL, Jin M, Yang SZ, Zheng XL, Liu YH. RED: RFID-based eccentricity detection for high-speed rotating machinery. IEEE
Trans. on Mobile Computing, 2021, 20(4): 1590–1601. [doi: 10.1109/TMC.2019.2962770]
[47] Wang J, Xiong J, Chen XJ, Jiang HB, Balan RK, Fang DY. TagScan: Simultaneous target imaging and material identification with
commodity RFID devices. In: Proc. of the 23rd Annual Int’l Conf. on Mobile Computing and Networking. Snowbird: ACM, 2017.
288–300. [doi: 10.1145/3117811.3117830]
[48] Zhao L, Xu JR, Yao YJ, Huang S. Liquid material identification based on RFID passive sensing and machine learning. In: Proc. of the
2023 Int’l Conf. on Artificial Intelligence of Things and Systems. Xi’an: IEEE, 2023. 79–85. [doi: 10.1109/AIoTSys58602.2023.00033]
[49] Lin YC, Xie L, Wang CY, Bu YL, Lu SL. DropMonitor: Millimeter-level sensing for RFID-based infusion drip rate monitoring. Proc. of
the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2021, 5(2): 72. [doi: 10.1145/3463496]
[50] Tajin MAS, Hossain MS, Mongan WM, Dandekar KR. Passive UHF RFID-based real-time intravenous fluid level sensor. IEEE Sensors
Journal, 2024, 24(3): 3863–3873. [doi: 10.1109/JSEN.2023.3342129]
[51] Li BB, Wang Y, Zhao YQ, Liu WY. Enabling fine-grained residual liquid height estimation with passive RFID tags. IEEE Sensors
Journal, 2023, 23(17): 20159–20168. [doi: 10.1109/JSEN.2023.3295842]
[52] Guo JC, Wang T, He Y, Jin M, Jiang CK, Liu YH. TwinLeak: RFID-based liquid leakage detection in industrial environments. In: Proc.
of the 2019 IEEE Conf. on Computer Communications. Paris: IEEE, 2019. 883–891. [doi: 10.1109/INFOCOM.2019.8737621]
[53] Zhao Y, Li XL, Zhang L. RFID based item-level leaking sensing in densely deployed environments. In: Proc. of the 4th Int’l Conf. on
Information Science, Parallel and Distributed Systems. Guangzhou: IEEE, 2023. 509–512. [doi: 10.1109/ISPDS58840.2023.10235521]
[54] Nikitin P, Brewster M, Kim J, Rao K. Dielectric sensing using T-matched RAIN RFID tags. In: Proc. of the 2023 IEEE Int’l Conf. on
RFID. Seattle: IEEE, 2023. 42–47. [doi: 10.1109/RFID58307.2023.10178643]
[55] Lv SB, Hong HK, Yang LQ, Ding JW, Song RH. Solving in-door human activity recognition via RFID based on unsupervised domain
adaptation. In: Proc. of the 4th IEEE Int’l Conf. on Power, Intelligent Computing and Systems. Shenyang: IEEE, 2022. 388–392. [doi: 10.
1109/ICPICS55264.2022.9873745]
[56] Wang ZY, Chen YH, Zheng H, Liu M, Huang P. Body RFID skeleton-based human activity recognition using graph convolution neural
network. IEEE Trans. on Mobile Computing, 2024, 23(6): 7301–7317. [doi: 10.1109/TMC.2023.3333043]
[57] Xie L, Wang CY, Liu AX, Sun JQ, Lu SL. Multi-touch in the air: Concurrent micromovement recognition using RF signals. IEEE/ACM
Trans. on Networking, 2018, 26(1): 231–244. [doi: 10.1109/TNET.2017.2772781]
[58] Liu J, Chen XY, Chen SG, Liu XL, Wang YY, Chen LJ. TagSheet: Sleeping posture recognition with an unobtrusive passive tag matrix.
In: Proc. of the 2019 IEEE Conf. on Computer Communications. Paris: IEEE, 2019. 874–882. [doi: 10.1109/INFOCOM.2019.8737599]
[59] Sun W. RFitness: Enabling smart yoga mat for fitness posture detection with commodity passive RFIDs. In: Proc. of the 2021 IEEE Int’l
Conf. on RFID. Atlanta: IEEE, 2021. 1–8. [doi: 10.1109/RFID52461.2021.9444325]
[60] Ali K, Liu AX, Chai E, Sundaresan K. Monitoring browsing behavior of customers in retail stores via RFID imaging. IEEE Trans. on
Mobile Computing, 2022, 21(3): 1034–1048. [doi: 10.1109/TMC.2020.3019652]
[61] Zhao CX, Wang L, Xiong F, Chen SG, Su J, Xu H. RFID-based human action recognition through spatiotemporal graph convolutional
neural network. IEEE Internet of Things Journal, 2023, 10(22): 19898–19912. [doi: 10.1109/JIOT.2023.3282680]
[62] Qiu Q, Wang TC, Chen FL, Wang CT. LD-recognition: Classroom action recognition based on passive RFID. IEEE Trans. on
Computational Social Systems, 2024, 11(1): 1182–1191. [doi: 10.1109/TCSS.2023.3234423]