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i2tag: RFID Mobility and Activity Identification Through Intelligent Profiling

Published:18 September 2017Publication History
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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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        • 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
          Issue’s Table of Contents

          Copyright © 2017 ACM

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          Publication History

          • Published: 18 September 2017
          • Accepted: 1 December 2016
          • Revised: 1 November 2016
          • Received: 1 September 2016
          Published in tist Volume 9, Issue 1

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