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.