A Caching-Enabled Permissioned Blockchain Scheme for Industrial Internet of Things Based on Deep Reinforcement Learning

The integration of the industrial internet of things (IIoT) and blockchain has become a popular concept that provides IIoT with a trustworthy computing environment. Numerous IIoT nodes together form a decentralized network with rich location-aware computation resources, which can offer great data pr...

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Bibliographic Details
Main Authors: Li, C. (Author), Li, X. (Author), Liu, P. (Author), Yao, C. (Author), Zhang, S. (Author)
Format: Article
Language:English
Published: Hindawi Limited 2023
Subjects:
Online Access:View Fulltext in Publisher
LEADER 02792nam a2200421Ia 4500
001 10.1155-2023-2852085
008 230526s2023 CNT 000 0 und d
020 |a 15308669 (ISSN) 
245 1 0 |a A Caching-Enabled Permissioned Blockchain Scheme for Industrial Internet of Things Based on Deep Reinforcement Learning 
260 0 |b Hindawi Limited  |c 2023 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1155/2023/2852085 
520 3 |a The integration of the industrial internet of things (IIoT) and blockchain has become a popular concept that provides IIoT with a trustworthy computing environment. Numerous IIoT nodes together form a decentralized network with rich location-aware computation resources, which can offer great data processing capabilities and low-latency services. However, we still face the challenges of how to efficiently process the massive IIoT data on resource-constrained IIoT nodes by blockchain smart contracts, as their storage capacity only allows them to store limited blockchain data. This work is aimed at improving the smart contract execution efficiency on these IIoT nodes by caching based on deep reinforcement learning. On the one hand, focusing on the characteristics of IIoT, the ledger structure, network architecture, and transaction flow are optimized. IIoT nodes are enabled to store and cache part of block data without affecting global data consistency. On the other hand, we formulated the blockchain caching problem as a Markov decision process and implemented a lightweight caching agent based on deep Q-learning. Proper features and a reward function are defined to minimize the execution delay of smart contracts. The extensive experimental results show that our proposed scheme can effectively reduce the data dissemination costs and smart contract execution delays of IIoT nodes that hold limited blockchain data. © 2023 Peng Liu et al. 
650 0 4 |a Blockchain 
650 0 4 |a Block-chain 
650 0 4 |a Computation resources 
650 0 4 |a Computing environments 
650 0 4 |a Contract execution 
650 0 4 |a Data handling 
650 0 4 |a Decentralized networks 
650 0 4 |a Deep learning 
650 0 4 |a Digital storage 
650 0 4 |a Distributed ledger 
650 0 4 |a Internet of things 
650 0 4 |a Location-aware 
650 0 4 |a Low latency 
650 0 4 |a Markov processes 
650 0 4 |a Network architecture 
650 0 4 |a Processing capability 
650 0 4 |a Reinforcement learning 
650 0 4 |a Reinforcement learnings 
650 0 4 |a Smart contract 
650 0 4 |a Storage capacity 
700 1 0 |a Li, C.  |e author 
700 1 0 |a Li, X.  |e author 
700 1 0 |a Liu, P.  |e author 
700 1 0 |a Yao, C.  |e author 
700 1 0 |a Zhang, S.  |e author 
773 |t Wireless Communications and Mobile Computing  |x 15308669 (ISSN)  |g 2023