LoRa Device Fingerprinting in the Wild: Disclosing RF Data-Driven Fingerprint Sensitivity to Deployment Variability

Abstract

Deep learning-based fingerprinting techniques have recently emerged as potential enablers of various wireless applications. However, their resiliency to time, location, and/or configuration changes in the operating environment undoubtedly remains one major challenge that lies ahead in their deployment pathway. In this paper, we present an experimental framework that aims to disclose, understand and overcome the sensitivity of LoRa device fingerprinting to variations in deployment settings. We first began by presenting our RF fingerprinting datasets, collected from 25 different LoRa devices. The datasets cover a comprehensive set of experimental scenarios, considering both indoor and outdoor environments with varying network deployment settings, such as varying the distance between the transmitters and the receiver, the configuration of the LoRa protocol, the physical location of the conducted experiment, and the receiver hardware used for capturing the fingerprints. We then proposed a new technique that leverages out-of-band spectrum distortions, that are caused by device-specific hardware impairments, to provide unique device signatures that we exploit to improve fingerprinting accuracy. Finally, we conducted an experimental study that discloses the sensitivity of deep learning-based RF fingerprinting to changes in various deployment settings while considering three data representations of the learning model input: time-domain IQ, frequency-domain FFT, and Amplitude/Phase polar-coordinate. We found that the learning models perform relatively well when trained and tested under the same deployment settings, with FFT representation yielding the best performance followed by IQ representation. However, when trained and tested under different settings, the models (i) fail to maintain their high accuracy when the channel conditions change, and (ii) completely lose their ability to classify devices when the LoRa configuration and/or the USRP receiver hardware change. In addition, we interestingly observed that FFT representation performs exceptionally poorly when training and testing are done under different deployment settings, regardless of the type of the setting change.

Keywords

• Device fingerprinting
• LoRa datasets
• deep learning
• experimental study
• hardware impairments