research-article

i²tag: RFID Mobility and Activity Identification Through Intelligent Profiling

Authors:

Jiangchuan LiuAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 9, Issue 1

Article No.: 5, Pages 1 - 21

https://doi.org/10.1145/3035968

Published: 18 September 2017 Publication History

Abstract

Many radio frequency identification (RFID) applications, such as virtual shopping cart and tag-assisted gaming, involve sensing and recognizing tag mobility. However, existing RFID localization methods are mostly designed for static or slowly moving targets (less than 0.3m/sec). More importantly, we observe that prior methods suffer from serious performance degradation for detecting real-world moving tags in typical indoor environments with multipath interference. In this article, we present i²tag, an intelligent mobility-aware activity identification system for RFID tags in multipath-rich environments (e.g., indoors). i²tag employs a supervised learning framework based on our novel fine-grain mobility provile, which can quantify different levels of mobility. Unlike previous methods that mostly rely on phase measurement, i²tag takes into account various measurements, including RSSI variance, packet loss rate, and our novel relative phase--based fingerprint. Additionally, we design a multidimensional dynamic time warping--based algorithm to robustly detect mobility and the associated activities. We show that i²tag is readily deployable using off-the-shelf RFID devices. A prototype has been implemented using a ThingMagic reader and standard-compatible tags. Experimental results demonstrate its superiority in mobility detection and activity identification in various indoor environments.

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Cited By

Sheng BHan RXiao FGuo ZGui L(2024)MetaFormerProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435508:1(1-27)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643550
Sheng BHan RCai HXiao FGui LGuo Z(2024)CDFi: Cross-Domain Action Recognition Using WiFi SignalsIEEE Transactions on Mobile Computing10.1109/TMC.2023.334893923:8(8463-8477)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TMC.2023.3348939
Liu MChen YZheng HHuang LZhao KZhao Y(2024)Skeleton-Based Human Activities Fine-grained Recognition with RFID Technology2024 9th International Conference on Signal and Image Processing (ICSIP)10.1109/ICSIP61881.2024.10671507(6-12)Online publication date: 12-Jul-2024
https://doi.org/10.1109/ICSIP61881.2024.10671507
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Index Terms

i²tag: RFID Mobility and Activity Identification Through Intelligent Profiling
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Sensor networks
2. Networks
  1. Network properties
    1. Network mobility
  2. Network types
    1. Cyber-physical networks
      1. Sensor networks

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 9, Issue 1

Regular Papers and Special Issue: Data-driven Intelligence for Wireless Networking

January 2018

258 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3134224

Editor:
Yu Zheng
Microsoft Research, China

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Copyright © 2017 ACM.

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Publication History

Published: 18 September 2017

Accepted: 01 December 2016

Revised: 01 November 2016

Received: 01 September 2016

Published in TIST Volume 9, Issue 1

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Strategic Project Grant
NSERC Discovery Grant
Industrial Canada Technology Demonstration Program (TDP)
E.W. R. Steacie Memorial Fellowship

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Cited By

Sheng BHan RXiao FGuo ZGui L(2024)MetaFormerProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435508:1(1-27)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643550
Sheng BHan RCai HXiao FGui LGuo Z(2024)CDFi: Cross-Domain Action Recognition Using WiFi SignalsIEEE Transactions on Mobile Computing10.1109/TMC.2023.334893923:8(8463-8477)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TMC.2023.3348939
Liu MChen YZheng HHuang LZhao KZhao Y(2024)Skeleton-Based Human Activities Fine-grained Recognition with RFID Technology2024 9th International Conference on Signal and Image Processing (ICSIP)10.1109/ICSIP61881.2024.10671507(6-12)Online publication date: 12-Jul-2024
https://doi.org/10.1109/ICSIP61881.2024.10671507
Liu LZhou ZChen MGong W(2024)CAT: Cross-Adversarial Training for WiFi-Based Human Activity Recognition2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD61410.2024.10580434(1023-1028)Online publication date: 8-May-2024
https://doi.org/10.1109/CSCWD61410.2024.10580434
Wang ZChen YZheng HLiu MHuang P(2023)Body RFID Skeleton-Based Human Activity Recognition Using Graph Convolution Neural NetworkIEEE Transactions on Mobile Computing10.1109/TMC.2023.333304323:6(7301-7317)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1109/TMC.2023.3333043
Huang ZLuo CZheng YHuang KYang ZYang Y(2023)Radio Spatiotemporal Pheromone for RSS-Based Device-Free Activity Recognition7th International Conference on Computing, Control and Industrial Engineering (CCIE 2023)10.1007/978-981-99-2730-2_70(745-756)Online publication date: 21-Jul-2023
https://doi.org/10.1007/978-981-99-2730-2_70
Zhou ZWang FYu JRen JWang ZGong W(2022)Target-oriented Semi-supervised Domain Adaptation for WiFi-based HARIEEE INFOCOM 2022 - IEEE Conference on Computer Communications10.1109/INFOCOM48880.2022.9796782(420-429)Online publication date: 2-May-2022
https://dl.acm.org/doi/10.1109/INFOCOM48880.2022.9796782
Zhao YLi JLiu W(2022)Height Estimation at Entrance With Passive RFIDs: Possibility, Design and ImplementationIEEE Access10.1109/ACCESS.2022.319050010(76470-76479)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3190500
Gupta NGupta SPathak RJain VRashidi PSuri J(2022)Human activity recognition in artificial intelligence framework: a narrative reviewArtificial Intelligence Review10.1007/s10462-021-10116-x55:6(4755-4808)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s10462-021-10116-x
Chang XDai JZhang ZZhu KXing G(2021)RF-RVM: Continuous Respiratory Volume Monitoring With COTS RFID TagsIEEE Internet of Things Journal10.1109/JIOT.2021.30637188:16(12892-12901)Online publication date: 15-Aug-2021
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