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Energy-Aware Blockchain Resource Allocation Algorithm with Deep Reinforcement Learning for Trusted Authentication

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Smart Grid and Innovative Frontiers in Telecommunications (SmartGIFT 2020)

Abstract

Internet of things (IoT) technology is in continuous development, and the access of the IoT power terminal is facing various security threats such as data tampering and malicious attacks. Thus, we propose a blockchain-based edge-terminal collaborative resource allocation architecture to solve these security problems, which places the terminal trusted authentication data on the blockchain to realize the security of the terminal authentication information. Since the mining process of the blockchain system will generate a large number of computing intensive tasks, this paper establishes an energy-oriented blockchain mining task offloading model, and proposes the energy-aware blockchain resource allocation (EABRA) algorithm with deep reinforcement learning (DRL) to jointly optimize the offloading decision and transmission power allocation decision. Finally, the simulation results show that the EABRA algorithm can save 68.87% energy consumption than the Random algorithm, which verifies the correctness and feasibility of the scheme.

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Acknowledgment

This work is supported by the Science and Technology Project of State Grid Corporation of China: Research on Key Technologies of dynamic identity security authentication and risk control in power business (SGHEXT00YJJS1900050).

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Correspondence to Boxian Liao .

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Gao, L., Zhang, X., Liu, T., Yang, H., Liao, B., Guo, J. (2021). Energy-Aware Blockchain Resource Allocation Algorithm with Deep Reinforcement Learning for Trusted Authentication. In: Cheng, M., Yu, P., Hong, Y., Jia, H. (eds) Smart Grid and Innovative Frontiers in Telecommunications. SmartGIFT 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 373. Springer, Cham. https://doi.org/10.1007/978-3-030-73562-3_8

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  • DOI: https://doi.org/10.1007/978-3-030-73562-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73561-6

  • Online ISBN: 978-3-030-73562-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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