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URL: https://pubmed.ncbi.nlm.nih.gov/29904003/

⇱ A Novel Friendly Jamming Scheme in Industrial Crowdsensing Networks against Eavesdropping Attack - PubMed

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Abstract

Eavesdropping attack is one of the most serious threats in industrial crowdsensing networks. In this paper, we propose a novel anti-eavesdropping scheme by introducing friendly jammers to an industrial crowdsensing network. In particular, we establish a theoretical framework considering both the probability of eavesdropping attacks and the probability of successful transmission to evaluate the effectiveness of our scheme. Our framework takes into account various channel conditions such as path loss, Rayleigh fading, and the antenna type of friendly jammers. Our results show that using jammers in industrial crowdsensing networks can effectively reduce the eavesdropping risk while having no significant influence on legitimate communications.

Keywords: crowdsensing; friendly jamming; industrial internet of things; security.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

👁 Figure 1
Figure 1
Network model.
👁 Figure 2
Figure 2
Friendly Jammers with Directional Antennas.
👁 Figure 3
Figure 3
Geometrical relationship of the friendly jammers and the eavesdropper.
👁 Figure 4
Figure 4
Geometrical relationship of friendly jammers (three jammers are shown).
👁 Figure 5
Figure 5
and with DFJ scheme and OFJ scheme versus NFJ scheme when , , and M varies from 1 to 10. (a) Probability of successful transmission ; (b) Probability of eavesdropping attack .
👁 Figure 6
Figure 6
and with the DFJ scheme and the OFJ scheme versus the NFJ scheme when , , and N varies from 1 to 16. (a) Probability of successful transmission ; (b) Probability of eavesdropping attack .
👁 Figure 7
Figure 7
and with DFJ scheme and OFJ scheme versus NFJ scheme when , , , , SINR threshold T and varies from to . (a) Probability of successful transmission ; (b) Probability of eavesdropping attacks .
👁 Figure 8
Figure 8
Probability of eavesdropping attacks with DFJ scheme and OFJ scheme versus NFJ scheme when with distance D ranging from 2 to 20. (a) ; (b) .
👁 Figure 9
Figure 9
Eavesdropper inside of the network.
👁 Figure 10
Figure 10
Impact of friendly jammers on other networks.

References

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    1. Datta S.K., da Costa R.P.F., Bonnet C., Hrri J. oneM2M Architecture Based Iot Framework for Mobile Crowd Sensing in Smart Cities; Proceedings of the 2016 European Conference on Networks and Communications (EuCNC); Athens, Greece. 27–30 June 2016.
    1. Pilloni V. How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0. Future Intern. 2018;10:24. doi: 10.3390/fi10030024. - DOI
    1. Shu L., Chen Y., Huo Z., Bergmann N., Wang L. When Mobile Crowd Sensing Meets Traditional Industry. IEEE Access. 2017;5:15300–15307. doi: 10.1109/ACCESS.2017.2657820. - DOI

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