MobRFFI: A WiFi RF Fingerprinting Dataset with Granular Multi-Receiver Signal Capture

Citation Author(s):
Stepan
Mazokha
Florida Atlantic University
Fanchen
Bao
Florida Atlantic University
George
Sklivanitis
Florida Atlantic University
Jason
Hallstrom
Florida Atlantic University
Submitted by:
Georgios Sklivanitis
Last updated:
Tue, 01/21/2025 - 16:06
DOI:
10.21227/xyav-rh42
Data Format:
*.dat, *.json
License:
Creative Commons Attribution
30 Views
Categories:
Wireless Networking
Digital signal processing
IoT
Machine Learning
Keywords:
WiFi, Device Authentication, Radio Frequency Fingerprint Identification, Deep Learning, IQ samples, USRP
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Abstract 

MobRFFI is a WiFi device fingerprinting and re-identification dataset collected in the Orbit testbed facility in July and April 2024. The dataset contains raw IQ samples of WiFi transmissions captured at 25 Msps on channel 11 (2462 MHz) in the 2.4 GHz band, using Ettus Research N210r4 USRPs as receivers and a set of WiFi nodes equipped with Atheros AR5212 chipsets as transmitters. The data collection spans two days (July 19 and August 8, 2024) and includes 12,068 capture files totaling 5.7 TB of data. Each capture file contains a two-second signal capture, during which we performed a transfer of randomly generated data via UDP protocol between a transmitter and a WiFi access point, with the USRP receiver performing independent signal capture.

The dataset has several key advantages that may be useful for developing novel WiFi-based RFFI methods. First, we perform signal capture simultaneously across multiple USRP receivers (4 on day 1 and 3 on day 2). Second, we perform repeated rounds of signal capture, which is useful for evaluating method performance on multiple hours (24 hours on day 1, and 4 hours on day 2). Third, we perform signal capture on two separate days, with sufficient sensor overlap for evaluating multi-day method performance. Finally, we provide a suite of signal processing tools and a reduced-size dataset for faster onboarding and experimentation.

For more details, please refer to our GitHub repository: https://github.com/i-sense/mobrffi-paper

If you find this dataset useful, please consider citing our recent publication: "MobRFFI: WiFi Device Fingerprinting and Re-identification for Mobility Intelligence."

Instructions: 

Please, refer to our GitHub repository for detailed documentation about the dataset and the associated decoding and evaluation tools: https://github.com/i-sense/mobrffi-paper

Funding Agency: 
National Science Foundation
Grant Number: 
EEC-2133516

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