Committee Chair

Reising, Donald R.

Committee Member

Ofoli, Abdul R.; Loveless, Thomas D.

Department

Dept. of Electrical Engineering

College

College of Engineering and Computer Science

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

The Internet of Things (IoT) consists of many electronic and electromechanical devices connected to the Internet. It is estimated that the number of connected IoT devices will be between 20 and 50 billion by the year 2020. The need for mechanisms to secure IoT networks will increase dramatically as 70% of the edge devices have no encryption. Previous research has proposed RF-DNA fingerprinting to provide wireless network access security through the exploitation of PHY layer features. RF-DNA fingerprinting takes advantage of unique and distinct characteristics that unintentionally occur within a given radio’s transmit chain during waveform generation. In this work, the application of RF-DNA fingerprinting is extended by developing a Nelder-Mead-based algorithm that estimates the coefficients of an indoor Rayleigh fading channel. The performance of the Nelder-Mead estimator is compared to the Least Square estimator and is assessed with degrading signal-to-noise ratio. The Rayleigh channel coefficients set estimated by the Nelder-Mead estimator is used to remove the multipath channel effects from the radio signal. The resulting channel-compensated signal is the region where the RF-DNA fingerprints are generated and classified. For a signal-to-noise ratio greater than 21 decibels, an average percent correct classification of more than 95% was achieved in a two-reflector channel.

Acknowledgments

Firstly, I would like to express my sincere gratitude to my advisor Dr. Reising for the continuous support of my Thesis study and related research, for his patience, motivation, and immense knowledge. His guidance helped me over the research and in writing of this thesis. Besides my advisor, I would like to thank the rest of my thesis committee: Dr. Loveless, and Dr. Ofoli, for their insightful comments and encouragement. I would also like to thank the Department of Electrical Engineering, University of Tennessee, Chattanooga (UTC), for all the support and the great professors provided.

Degree

M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.

Date

8-2018

Subject

Radio frequency identification systems; Internet of things; Wireless communication systems

Keyword

Multipath; Rayleigh; Fingerprinting; Nelder-Mead; Least-Square; Wireless

Document Type

Masters theses

Extent

xi, 69 leaves

Language

English

Rights

Under copyright.

License

http://creativecommons.org/licenses/by-nc-nd/3.0/

Date Available

3-1-2019

Available for download on Friday, March 01, 2019

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