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- W4387054220 abstract "Blockchain-Enabled Industrial Internet of Things (IIoT) networks are a new norm in global developments. This paper discusses Blockchain-Enabled IIoT network challenges and the role of Reinforcement Learning techniques in offering solutions for those challenges. This paper provides (i) fundamental frameworks of Blockchain and IIoT networks, (ii) a sketch of RL techniques, e.g., Q-Learning, Multi-armed Bandit Learning, and Actor-Critic Learning, (iii) demonstrates Blockchain-Enabled IIoT networks concerning IIoT challenges, and (iv) Blockchain realizable solutions that improve IIoT. This study finds that Blockchain improves security, data integrity, and autonomy in IIoT networks. Moreover, it reduces the cost of instrument IIoT networks and builds trust among users. A blockchain-enabled most crucial solution materializes that Smart contracts facilitate transactions between parties. However, the Blockchain-Enabled IIoT network nevertheless faces diverse issues. Most importantly, the system needs to be optimized regarding forking events, energy, time to finality, transaction throughput, link security, and average Blocktime. Consequently, RL techniques such as Q-Learning plausibly provide solutions for these issues and improve the performance and efficiency of networks." @default.
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- W4387054220 date "2023-01-01" @default.
- W4387054220 modified "2023-09-27" @default.
- W4387054220 title "Reinforcement Learning in Blockchain-Enabled IIoT Networks" @default.
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- W4387054220 doi "https://doi.org/10.1007/978-3-031-43145-6_19" @default.
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