TagFree: Passive object differentiation via physical layer radiometric signatures

Y Zou, Y Wang, S Ye, K Wu… - 2017 IEEE International …, 2017 - ieeexplore.ieee.org
Y Zou, Y Wang, S Ye, K Wu, LM Ni
2017 IEEE International Conference on Pervasive Computing and …, 2017ieeexplore.ieee.org
Object differentiation plays a vital role in our daily life and such systems are widely deployed
with RFID tags or bar codes attached on goods. In certain scenarios, however, attaching
tags to objects may be impractical due to cost and protection issues. In this paper, we
propose TagFree, a novel object differentiation scheme without attaching tags. Instead of
relying on external tags, we exploit the inherent radiometric properties of different objects as
their signatures. To improve the robustness and efficiency of TagFree, we empirically …
Object differentiation plays a vital role in our daily life and such systems are widely deployed with RFID tags or bar codes attached on goods. In certain scenarios, however, attaching tags to objects may be impractical due to cost and protection issues. In this paper, we propose TagFree, a novel object differentiation scheme without attaching tags. Instead of relying on external tags, we exploit the inherent radiometric properties of different objects as their signatures. To improve the robustness and efficiency of TagFree, we empirically determine a spatial safe zone and harness successive cancellation to distinguish multiple objects simultaneously. We prototype TagFree on commercial WiFi infrastructure and evaluate its performance in various indoor scenarios. Experimental results demonstrate that TagFree achieves single object distinguishing accuracy of 96% measured at the same location, and over 80% within the safe zone range of up to 3m along a 7m link. TagFree can also differentiate up to 3 objects with acceptable accuracy.
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