University of Tennessee at Chattanooga
The impact of Rayleigh fading channel effects on the RF-DNA fingerprinting process
Abstract
dc:description.abstractThe 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.
Degree
thesis:*- Grantor dc:publisher
- University of Tennessee at Chattanooga
- Year dc:date.available
- 2019
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Fadul, Mohamed
- Contributors dc:contributor
-
- Reising, Donald R.
- Ofoli, Abdul R.; Loveless, Thomas D.
- College of Engineering and Computer Science
Subjects
dc:subject × 3Rights
dc:rights- Language dc:language
- English, eng
Identifiers
dc:identifier.*- Repository record dc:identifier
- https://scholar.utc.edu/theses/570
- OAI identifier oai:identifier
- oai:scholar.utc.edu:theses-1726