RF-Fingerprint-BT-IoT: Real-world Frequency Hopping Bluetooth dataset from IoT devices for RF fingerprinting

A real-world radio frequency (RF) fingerprinting dataset for commercial off-the-shelf (COTS) Bluetooth emitters under challenging testbed setups is presented in this dataset. The dataset includes emissions from 10 COTS IoT emitters (2 laptops and 8 commercial chips) that are captured with a National Instruments Ettus USRP X300 radio outfitted with a UBX160 daughterboard and a VERT2450 antenna. The receiver is tuned to record a 2 MHz bandwidth of the spectrum centered at the 2.414 GHz frequency. This is a first-of-its-kind dataset for fingerprinting Bluetooth emitters under challenging and diverse indoor laboratory setups. The dataset is split into two: Day1 and Day2 each of which is recorded in a different time frame, location, and testbed setup to enable critical generalization test of the trained deep learning (DL) model. The authors suggest training the DL model with the Day1 dataset which is a simpler setup with recordings under varied Bluetooth signal strengths followed by evaluating the generalization capability of the model with Day2 dataset which is a challenging and vastly different setup compared to Day1. This evaluation will validate the real-world deployment capability of the trained DL model. The dataset follows the SigMF specifications with certain field extensions to facilitate the fingerprinting application and include additional metadata fields. Each capture is of length 40 Mega Samples (MS) and is associated with a JSON metadata file.

本数据集提供了面向商用现货（commercial off-the-shelf, COTS）蓝牙发射设备的真实场景射频（radio frequency, RF）指纹数据集，实验设置兼具挑战性。数据集包含10台COTS物联网（IoT）发射设备的射频发射信号，其中涵盖2台笔记本电脑与8款商用芯片，采集设备为搭载UBX160子板与VERT2450天线的美国国家仪器（National Instruments）Ettus USRP X300软件定义无线电。接收端调谐至中心频率为2.414 GHz的频段，录制2 MHz带宽的射频信号。本数据集是同类首个针对挑战性多样化室内实验室测试场景的蓝牙发射设备指纹识别专用数据集。数据集分为Day1与Day2两个子集，二者分别在不同时间、地点与测试平台设置下采集，用于支撑训练后的深度学习（deep learning, DL）模型的关键泛化测试。作者建议使用Day1数据集训练深度学习模型：该子集的采集场景更为简单，且覆盖了不同强度的蓝牙信号；随后通过Day2数据集评估模型的泛化能力——Day2的测试场景与Day1差异显著且更具挑战性。该评估流程可验证训练完成的深度学习模型在真实场景中的部署能力。本数据集遵循SigMF规范，并新增了部分字段扩展以适配指纹识别应用，同时包含额外的元数据字段。每段采集数据长度为40兆采样（Mega Samples, MS），并附带对应的JSON元数据文件。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于射频指纹识别的真实世界蓝牙数据集，包含来自10种商用物联网设备的信号发射数据，分为Day1（简单环境）和Day2（复杂环境）两部分，旨在评估深度学习模型的泛化能力。数据以SigMF规范存储，包含二进制捕获文件和JSON元数据文件。