Carrier Frequency Offset in Internet-of-Things Radio Frequency Fingerprint Identification: An Experimental Review

Radio frequency fingerprint (RFF) identification has become a promising security solution for resource-constrained Internet-of-Things (IoT) devices, which relies on hardware impairments-induced radio frequency features for identification; among which, a hotspot feature is the carrier frequency offset (CFO). Existing research, however, advocates contradictory perspectives on the usage of CFO: For identification and for compensation; the former employs CFO in the feature space while the latter eliminates the CFO from the feature space, both for improving the RFF identification accuracy. In this review, we first discuss the RFF identification procedures and investigate the origination of the CFO and further its relationship with the clock skew of the crystal oscillator. We then provide a review of the state-of-the-art RFF identification schemes, in two categories respectively employing CFO for identification and compensation. Finally, on a real testbed, we experimentally investigate the impact of the usage of CFO on RFF identification accuracy. Experimental results reveal that, the stabilities of CFOs are quite different on hardware platforms from different manufacturers; CFOs can be used for identification when they are relatively distinguishable; compensating CFO alone is inadequate for long-term identification.