i 2 tag

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 i 2 tag, an intelligent mobility-aware activity identification system for RFID tags in multipath-rich environments (e.g., indoors). i 2 tag 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, i 2 tag 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 i 2 tag 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.