"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."

本研究提出了一种基于射频指纹（Radio Frequency Fingerprinting, RFFI）的LoRa设备认证新型数据集，旨在解决资源受限环境下的物联网（Internet of Things, IoT）安全挑战。本数据集通过三种互补的表征形式采集了23台LoRa设备的硬件专属信号特征：原始IQ样本、快速傅里叶变换（Fast Fourier Transform, FFT）频谱与时频谱图。该数据集由搭载GPS同步功能的USRP B200接收机采集，包含粗粒度与细粒度载波频率偏移（Carrier Frequency Offset, CFO）估计值，以支撑更精准的特征分析。本数据集支持深度学习方法的应用，同时设置了面向设备认证的划分方式：20台用于训练、3台用于测试，以评估模型在真实场景下的泛化能力。本研究的核心贡献在于构建了一款专为LoRa网络设计的多模态射频指纹数据集。除核心数据集外，本研究还提供了集成化的CFO标注信息，可支撑混合式认证方法的研究。尤为重要的是，本研究还搭建了一套基于软件定义无线电的可复现实验框架，可为后续相关研究提供支持。上述多项贡献共同推动了物理层安全、轻量化设备认证以及高鲁棒性无线指纹识别系统领域的研究进展。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于LoRa设备射频指纹识别的多模态数据集，包含23个设备的三种信号表示形式，具有载波频率偏移标注和明确的训练/测试划分，旨在支持物联网物理层安全认证研究。