The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Performance Optimization for Blockchain-Enabled Industrial Internet of Things (IIoT) Systems: A Deep Reinforcement Learning Approach
Abstract
Recent advances in the industrial Internet of things (IIoT) provide plenty of opportunities for various industries. To address the security and efficiency issues of the massive IIoT data, blockchain is widely considered as a promising solution to enable data storing/processing/sharing in a secure and efficient way. To meet the high throughput requirement, this paper proposes a novel deep reinforcement learning (DRL)-based performance optimization framework for blockchain-enabled IIoT systems, the goals of which are threefold: 1) providing a methodology for evaluating the system from the aspects of scalability, decentralization, latency, and security; 2) improving the scalability of the underlying blockchain without affecting the system's decentralization, latency, and security; and 3) designing a modulable blockchain for IIoT systems, where the block producers, consensus algorithm, block size, and block interval can be selected/adjusted using the DRL technique. Simulations results show that our proposed framework can effectively improve the performance of blockchain-enabled IIoT systems and well adapt to the dynamics of the IIoT.
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Cites
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Cited by
Glavic, M. (2019) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2019.09.008 · Cited by 163
Blockchain-based trust mechanism for digital twin empowered Industrial Internet of Things
A., S., Vairavasundaram, S., Kotecha, K., V., I., Ravi, L., Selvachandran, G., & Abraham, A. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2022.11.002 · Cited by 156
Blockchain Intelligence for Internet of Vehicles: Challenges and Solutions
Wang, X., Zhu, H., Ning, Z., Guo, L., & Zhang, Y. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3305312 · Cited by 131
Tornado: Enabling Blockchain in Heterogeneous Internet of Things Through a Space-Structured Approach
Liu, Y., Wang, K., Qian, K., Du, M., & Guo, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2954128 · Cited by 82
Blockchains for Artificial Intelligence of Things: A Comprehensive Survey
Shen, M., Gu, A., Kang, J., Tang, X., Lin, X., Zhu, L., & Niyato, D. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3268705 · Cited by 54
Metadata
- Authors
- Liu, M., Yu, F. R., Teng, Y., Leung, V. C. M., & Song, M.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2019-06
- Volume
- 15
- Issue
- 6
- Pages
- 3559-3570
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2913127416
- Cited by
- 365
- References
- 34
- FWCI
- 62.48
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Liu, M., Yu, F. R., Teng, Y., Leung, V. C. M., & Song, M. (2019). Performance Optimization for Blockchain-Enabled Industrial Internet of Things (IIoT) Systems: A Deep Reinforcement Learning Approach. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2019.2897805
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