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Published on 22 March 2023
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Lai,Z. (2023). Effect of classical data signals on quantum key distribution in industrial internet of things. Applied and Computational Engineering,2,56-63.
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Effect of classical data signals on quantum key distribution in industrial internet of things

Zhenzhi Lai *,1,
  • 1 The University of Melbourne, Grattan Street, Parkiville, Victoria, 3010, Australia

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/2/20220544

Abstract

Facing the increasing need of data transmission security, encrypting data through cryptography is a key solution. In an industrial Internet of things, cryptography based on quantum key distribution provides perfect secrecy with lower resource requirements of both computational power and storage compared with traditional cryptography. To explore how to deploy this technology, this paper proposes an industrial Internet of things network architecture embedded with quantum key distribution systems, combines it with the noise model of spontaneous Raman scattering and the evaluation model of quantum key distribution systems theoretically, simulates the performance in a normally used industrial environment, and works out instructions to the deployment of the raised architecture. The results also show that a better choice to avoid performance descending is to duplicate classical channels and quantum channels with the same direction instead of moving classical channels backward, while noises have the strongest influence at the transmission distance of 25 km.

Keywords

quantum key distribution, industrial internet of things, quantum cryptography, spontaneous Raman scattering.

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Cite this article

Lai,Z. (2023). Effect of classical data signals on quantum key distribution in industrial internet of things. Applied and Computational Engineering,2,56-63.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

Conference website: https://www.confcds.org/
ISBN:978-1-915371-19-5(Print) / 978-1-915371-20-1(Online)
Conference date: 16 July 2022
Editor:Alan Wang
Series: Applied and Computational Engineering
Volume number: Vol.2
ISSN:2755-2721(Print) / 2755-273X(Online)

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