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Secure Transmission Method of Power Quality Data in Power Internet of Things Based on the Encryption Algorithm

Author

Listed:
  • Xin Liu

    (State Grid Shandong Electric Power Research Institute, China)

  • Yingxian Chang

    (State Grid Shandong Electric Power Company, China)

  • Honglei Yao

    (State Grid Shandong Electric Power Research Institute, China)

  • Bing Su

    (State Grid Shandong Electric Power Research Institute, China)

Abstract

As a new mobile communication technology in the era of the internet of things, 5G is characterized by high speed, low delay, and large connection. It is a network infrastructure to realize human-computer and internet of things in the era of the internet of things. Power quality data is the efficiency with which a power grid delivers electricity to users and expresses how well a piece of machinery uses the electricity it receives. The waveform at the nominal voltage and frequency is the goal of power quality research and improvement. The power internet of things (IoT) is an intelligent service platform that fully uses cutting-edge tech to enable user-machine interaction, data-driven decision-making, real-time analytics, and adaptive software design. The process by which plaintext is converted into cipher text is called an encryption algorithm. The cipher text may seem completely random, but it can be decrypted using the exact mechanism that created the encryption key.

Suggested Citation

  • Xin Liu & Yingxian Chang & Honglei Yao & Bing Su, 2023. "Secure Transmission Method of Power Quality Data in Power Internet of Things Based on the Encryption Algorithm," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 19(1), pages 1-19, January.
    • Handle: RePEc:igg:jdwm00:v:19:y:2023:i:1:p:1-19
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDWM.330014
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    References listed on IDEAS

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    1. Ronglai Shen & Qianxing Mo & Nikolaus Schultz & Venkatraman E Seshan & Adam B Olshen & Jason Huse & Marc Ladanyi & Chris Sander, 2012. "Integrative Subtype Discovery in Glioblastoma Using iCluster," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-9, April.
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