RF Fingerprinting for LoRa Device Authentication: Dataset Collection and Characterization

We present a novel dataset for LoRa device authentication using Radio Frequency Fingerprinting (RFFI), addressing IoT security challenges in resource-constrained environments. Our dataset captures hardware-specific signal characteristics from 23 LoRa devices through three complementary representations: raw IQ samples, FFT spectra, and time-frequency spectrograms. Collected using a USRP B200 receiver with GPS synchronization, the data incorporates both coarse and fine Carrier Frequency Offset (CFO) estimations for enhanced feature analysis. The dataset supports deep learning approaches while maintaining an authentication-focused partition (20 training\/3 testing devices) to evaluate real-world generalization. The primary contribution of this work is a multi-modal RF fingerprinting dataset specifically designed for LoRa networks. Beyond the core dataset, we further contribute an integrated CFO annotation, enabling hybrid authentication methods. Importantly, we also establish a reproducible experimental framework using software-defined radio that supports future research. Together, these contributions facilitate advancements in physical-layer security, lightweight device authentication, and robust wireless fingerprinting systems.