February 26, 2021
Dear Faculty, Graduate and Undergraduate Students,
You are cordially invited to my M.S. dissertation defense.
Candidate: Nicholas Smith
Degree: M.S., Electrical & Computer Engineering
Dissertation Title: Radio Frequency Dataset Construction for Device and Location Fingerprinting
Date and time: Tuesday, March 16, 2021, 1:00 PM
Venue: On-line Meeting via Webex (https://msstate.webex.com/meet/bt1071)
Dr. Bo Tang
Assistant Professor of Electrical and Computer Engineering
Dr. Maxwell Young
Assistant Professor of Computer Science and Engineering
Dr. John E. Ball
Associate Professor of Electrical and Computer Engineering
Radio-frequency (RF) fingerprinting is a process that uses the minute inconsistencies among manufactured radio transmitters to identify wireless devices. Coupled with location fingerprinting, which is a machine learning technique to locate devices based on their radio signals, it can uniquely identify and locate both trusted and rogue wireless devices transmitting over the air. This can have wide-ranging applications for the Internet of Things, security, and networking fields. To contribute to this effort, this research first builds a software-defined-radio (SDR) testbed to collect an RF dataset over LTE and WiFi channels. The developed testbed consists of both hardware which are receivers with multiple antennas and software which performs signal preprocessing. Several features that can be used for RF device fingerprinting and location fingerprinting, including received signal strength indicator, channel state information, and angle of arrival, are also extracted from the signals. With the developed dataset, several data-driven machine learning algorithms have been implemented and tested for fingerprinting performance evaluation. Overall, experimental results show promising performance with a radio fingerprinting accuracy above 90% and device localization within 1.10 meters.