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Deep Learning-Based Secure Transmission Strategy with Sensor-Transmission-Computing Linkage for Power Internet of Things

Bin Li*, Linghui Kong, Xiangyi Zhang, Bochuo Kou, Hui Yu, Bowen Liu

State Grid Beijing Urban District Power Supply Company, Beijing, 100034, China

* Corresponding Author: Bin Li. Email: email

Computers, Materials & Continua 2024, 78(3), 3267-3282. https://doi.org/10.32604/cmc.2024.047193

Abstract

The automatic collection of power grid situation information, along with real-time multimedia interaction between the front and back ends during the accident handling process, has generated a massive amount of power grid data. While wireless communication offers a convenient channel for grid terminal access and data transmission, it is important to note that the bandwidth of wireless communication is limited. Additionally, the broadcast nature of wireless transmission raises concerns about the potential for unauthorized eavesdropping during data transmission. To address these challenges and achieve reliable, secure, and real-time transmission of power grid data, an intelligent security transmission strategy with sensor-transmission-computing linkage is proposed in this paper. The primary objective of this strategy is to maximize the confidentiality capacity of the system. To tackle this, an optimization problem is formulated, taking into consideration interruption probability and interception probability as constraints. To efficiently solve this optimization problem, a low-complexity algorithm rooted in deep reinforcement learning is designed, which aims to derive a suboptimal solution for the problem at hand. Ultimately, through simulation results, the validity of the proposed strategy in guaranteed communication security, stability, and timeliness is substantiated. The results confirm that the proposed intelligent security transmission strategy significantly contributes to the safeguarding of communication integrity, system stability, and timely data delivery.

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Cite This Article

APA Style
Li, B., Kong, L., Zhang, X., Kou, B., Yu, H. et al. (2024). Deep learning-based secure transmission strategy with sensor-transmission-computing linkage for power internet of things. Computers, Materials & Continua, 78(3), 3267-3282. https://doi.org/10.32604/cmc.2024.047193
Vancouver Style
Li B, Kong L, Zhang X, Kou B, Yu H, Liu B. Deep learning-based secure transmission strategy with sensor-transmission-computing linkage for power internet of things. Comput Mater Contin. 2024;78(3):3267-3282 https://doi.org/10.32604/cmc.2024.047193
IEEE Style
B. Li, L. Kong, X. Zhang, B. Kou, H. Yu, and B. Liu, “Deep Learning-Based Secure Transmission Strategy with Sensor-Transmission-Computing Linkage for Power Internet of Things,” Comput. Mater. Contin., vol. 78, no. 3, pp. 3267-3282, 2024. https://doi.org/10.32604/cmc.2024.047193



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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