Real-world Commercial WiFi and Bluetooth Dataset for RF Fingerprinting

A real-world radio frequency (RF) fingerprinting dataset for commercial off-the-shelf (COTS) Bluetooth and WiFi emitters under challenging testbed setups is presented in this dataset. The chipsets within the devices (2 laptops and 8 commercial chips) are WiFi-Bluetooth combo transceivers. The emissions 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 66.67 MHz bandwidth of the spectrum centered at the 2.414 GHz frequency. This is a first-of-its-kind dataset for fingerprinting WiFi-Bluetooth combo chipsets that are commonly found in today's IoT era. The dataset is split into two for WiFi and Bluetooth - Day1 and Day2 - each of which is recorded in a different time frame but under the same receiver settings to enable a critical generalization test of the trained deep learning (DL) model. The authors suggest training the DL model with the Day1 dataset with recordings under varied Bluetooth and WiFi signal strengths followed by evaluating the generalization capability of the model with the Day2 dataset which is a challenging and different setup compared to Day1. This evaluation will validate the realistic 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.

本数据集呈现了一种针对商业现成（COTS）蓝牙和WiFi发射器在实际射频（RF）指纹识别场景下的真实世界数据集。设备内部集成的芯片组包括两台笔记本电脑和八款商用芯片，均为WiFi-Bluetooth组合收发器。使用配备UBX160子板和VERT2450天线的National Instruments Ettus USRP X300无线电设备捕捉了辐射。接收器被调谐以记录以2.414 GHz频率为中心的66.67 MHz频谱带宽。这是首个针对当今物联网时代普遍存在的WiFi-Bluetooth组合芯片集指纹识别的数据集。数据集分为WiFi和蓝牙两部分，分别为“Day1”和“Day2”，每部分均在不同的时间框架内记录，但采用相同的接收器设置，以便对训练的深度学习（DL）模型进行关键性泛化测试。作者建议使用包含不同蓝牙和WiFi信号强度记录的“Day1”数据集来训练深度学习模型，随后使用具有与“Day1”不同挑战性和设置条件的“Day2”数据集来评估模型的泛化能力。此评估将验证训练的深度学习模型在实际部署中的可行性。数据集遵循SigMF规范，并包含某些字段扩展以促进指纹识别应用，并包含额外的元数据字段。每个捕获长度为40兆样本（MS），并附有相应的JSON元数据文件。