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Classification of RFID Tags with Wavelet Fingerprinting

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Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint

Abstract

Passive radio frequency identification (RFID) tags lack the resources for standard cryptography, but are straightforward to clone. Identifying RF signatures that are unique to an emitter’s signal is known as physical-layer identification, a technique that allows for distinction between cloned devices. In this work, we study the effect real-world environmental variations have on the physical-layer fingerprints of passive RFID tags. Signals are collected for a variety of reader frequencies, tag orientations, and ambient conditions, and pattern classification techniques are applied to automatically identify these unique RF signatures. We show that identically programmed RFID tags can be distinguished using features generated from DWFP representations of the raw RF signals.

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Notes

  1. 1.

    Cambridge, MA (http://www.thingmagic.com).

  2. 2.

    St Louis, MI (http://www.lairdtech.com).

  3. 3.

    Mountain View, CA (http://www.ettus.com).

  4. 4.

    http://www.compsci.wm.edu/SciClone/.

  5. 5.

    MATLAB’s Image Processing Toolbox (MATLAB, 2008, The Mathworks, Natick, MA).

  6. 6.

    http://profs.sci.univr.it/~desena/FMT.

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Acknowledgements

This work was performed using computational facilities at the College of William and Mary which were provided with the assistance of the National Science Foundation, the Virginia Port Authority, Sun Microsystems, and Virginia’s Commonwealth Technology Research Fund. Partial support for the project is provided by the Naval Research Laboratory and the Virginia Space Grant Consortium. The authors would like to thank Drs. Kevin Rudd and Crystal Bertoncini for their many helpful discussions.

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  1. Department of Applied Science, William & Mary, Williamsburg, VA, USA

    Corey A. Miller & Mark K. Hinders

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Correspondence to Mark K. Hinders .

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Miller, C.A., Hinders, M.K. (2020). Classification of RFID Tags with Wavelet Fingerprinting. In: Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint. Springer, Cham. https://doi.org/10.1007/978-3-030-49395-0_7

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  • DOI: https://doi.org/10.1007/978-3-030-49395-0_7

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