LoRaSONN: A Novel Self-Operational Neural Network Learning Framework for RF-Fingerprint Identification of LoRa Devices

Radio-frequency (RF) fingerprint identification leverages the inherent unique transmitter hardware impairments to authenticate an emitter through an analysis of the received signal at the receiver. LoRa devices have gained widespread popularity in various Internet of Things (IoT) applications, primarily owing to their cost-effectiveness and impressive long-range communication capabilities. Recently, few studies have concentrated on the precise fingerprint identification of LoRa devices, however, these are focused on open-set authentication and security issues such as rogue device detection. In this paper, we propose a Self Operational Neural Network (SONN) learning framework, namely the LoRaSONN, for fingerprint identification of 60 distinct LoRa devices. The proposed framework exploits the spectrogram time-frequency representations (TFRs) obtained from the IQ samples of the preamble part of the received signals from the LoRa devices. The proposed framework achieves an overall accuracy of 97% over more than 8500 testing samples, across 60 devices from the same and different manufacturers evaluated on the recent publicly available LoRaRFFI dataset.