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A Dataset of physical-layer measurements in indoor wireless jamming scenarios

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submitted on 2023-11-06, 11:20 and posted on 2023-11-06, 11:30 authored by Saeif Alhazbi, Savio Sciancalepore, Gabriele Oligeri

The broadcast nature of wireless communications makes them vulnerable to denial-of-service attacks. Indeed, an adversary can prevent the reception of wireless messages by transmitting signals with high power over the same frequency of the considered channel. This paper presents an experimental dataset of real-world indoor communication scenarios affected by different jamming techniques. Specifically, our dataset includes data acquired from 7 different Software Defined Radios (SDRs), i.e., the USRP Ettus Research X310, operating in an office environment. Each experiment is characterized by a transmitter, a receiver, and a jammer. While the hardware of the transmitter and the receiver are kept the same for all the experiments, the hardware of the jammer is changed adopting 5 different radios of the same brand. The dataset includes different jamming behaviors, based on the type of signal injected by the jammer: no jamming (silent), tone (sinusoidal), and Gaussian noise. Moreover, besides having multiple jamming devices and modes, the dataset also includes different transmission distances and jamming powers. In each experiment, a pre-determined sequence of bits has been modulated using the BPSK scheme, transmitted wirelessly under different jamming conditions, and then stored, at the receiver, as a 2-columns matrix of I/Q samples. Researchers can use this dataset in several ways, including: (i) developing active and reactive techniques for jamming detection, (ii) jamming identification at the physical layer, and finally, (iii) developing mitigation techniques supported by real data.

Other Information

Published in: Data in Brief
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.dib.2022.108773

Funding

Open Access funding provided by the Qatar National Library

History

Language

  • English

Publisher

Elsevier

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU

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