# Bluetooth and WiFi Dataset for Real World RF Fingerprinting of Commercial Devices

arXiv: https://arxiv.org/abs/2303.13538 · HTML: https://mlsanity.com/paper/2303.13538/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [WiSig: A Large-Scale WiFi Signal Dataset for Receiver and Channel Agnostic RF Fingerprinting](https://mlsanity.com/paper/2112.15363/) (2021)
- [A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges](https://mlsanity.com/paper/2201.00680/) (2022)
- [Device Fingerprinting in Wireless Networks: Challenges and Opportunities](https://mlsanity.com/paper/1501.01367/) (2015)
- [Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression](https://mlsanity.com/paper/2203.00517/) (2022)
- [RF Fingerprinting Needs Attention: Multi-task Approach for Real-World WiFi and Bluetooth](https://mlsanity.com/paper/2209.03142/) (2022)
- [Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth](https://mlsanity.com/paper/2210.02897/) (2022)

## Built on by (most cited first, 1 of 1)

- [Physics-Informed Structure Anchoring With Capture-Aware Prototype Calibration for Cross-Environment RF Fingerprinting](https://mlsanity.com/paper/2607.09760/) (2026)

## Abstract (arXiv)

RF fingerprinting is emerging as a physical layer security scheme to identify illegitimate and/or unauthorized emitters sharing the RF spectrum. However, due to the lack of publicly accessible real-world datasets, most research focuses on generating synthetic waveforms with software-defined radios (SDRs) which are not suited for practical deployment settings. On other hand, the limited datasets that are available focus only on chipsets that generate only one kind of waveform. Commercial off-the-shelf (COTS) combo chipsets that support two wireless standards (for example WiFi and Bluetooth) over a shared dual-band antenna such as those found in laptops, adapters, wireless chargers, Raspberry Pis, among others are becoming ubiquitous in the IoT realm. Hence, to keep up with the modern IoT environment, there is a pressing need for real-world open datasets capturing emissions from these combo chipsets transmitting heterogeneous communication protocols. To this end, we capture the first known emissions from the COTS IoT chipsets transmitting WiFi and Bluetooth under two different time frames. The different time frames are essential to rigorously evaluate the generalization capability of the models. To ensure widespread use, each capture within the comprehensive 72 GB dataset is long enough (40 MSamples) to support diverse input tensor lengths and formats. Finally, the dataset also comprises emissions at varying signal powers to account for the feeble to high signal strength emissions as encountered in a real-world setting.