TY - JOUR
T1 - LoRa Device Fingerprinting in the Wild
T2 - Disclosing RF Data-Driven Fingerprint Sensitivity to Deployment Variability
AU - Elmaghbub, Abdurrahman
AU - Hamdaoui, Bechir
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Device fingerprinting
KW - LoRa datasets
KW - deep learning
KW - experimental study
KW - hardware impairments
UR - http://www.scopus.com/inward/record.url?scp=85118249118&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3121606
DO - 10.1109/ACCESS.2021.3121606
M3 - Article
AN - SCOPUS:85118249118
SN - 2169-3536
VL - 9
SP - 142893
EP - 142909
JO - IEEE Access
JF - IEEE Access
ER -