Radio frequency fingerprint identification for Internet of Things: A survey

Abstract

Radio frequency fingerprint (RFF) identification is a promising technique for identifying Internet of Things (IoT) devices. This paper presents a comprehensive survey on RFF identification, which covers various aspects ranging from related definitions to details of each stage in the identification process, namely signal preprocessing, RFF feature extraction, further processing and RFF identification. Specifically, three main steps of preprocessing are summarized, including carrier frequency offset estimation, noise elimination and channel cancellation. Besides, three kinds of RFFs are categorized, comprising I/Q signal-based, parameter-based and transformation-based features. Meanwhile, feature fusion and feature dimension reduction are elaborated as two main further processing methods. Furthermore, a novel framework is established from the perspective of closed set and open set problems, and the related state-of-the-art methodologies are investigated, including approaches based on traditional machine learning, deep learning and generative models. Additionally, we highlight the challenges faced by RFF identification and point out future research trends in this field.

Channel-Independent Fingerprints for Open-Set Wi-Fi Device Identification Using Wigner-Ville Distribution

“…12. The chosen optimal threshold, 𝜆, is the one with the lowest Equal Error Rate (EER) point, which means the point where false negative rate (FNR) and false positive rate(FPR) are equal [21].…”