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Enhanced Reinforcement Learning-Based Resource Scheduling for Secure Blockchain Networks in IIoT
Research Article | Published: 23 May 2025
IECE Transactions on Machine Intelligence Volume 1, Issue 1, 2025: 29-41
Volume 1, Issue 1, IECE Transactions on Machine Intelligence
Volume 1, Issue 1, 2025
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IECE Transactions on Machine Intelligence, Volume 1, Issue 1, 2025: 29-41

Research Article | 23 May 2025
Enhanced Reinforcement Learning-Based Resource Scheduling for Secure Blockchain Networks in IIoT
by
Meenakshi Garg Meenakshi Garg 1,2 *
1 Department of Computer Science, Government Bikram College of Commerce, Patiala, India
2 Higher Education Institute Society (HEIS), Government Bikram College of Commerce, Patiala, India
* Corresponding Author: Meenakshi Garg, [email protected]
DOI: 10.62762/TMI.2024.529242
Received: 23 December 2024, Accepted: 24 April 2025, Published: 23 May 2025  
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Abstract
To meet latency constraints, fog computing takes computational assets to the network edge. Blockchain and reinforcement learning are increasingly being integrated into the Industrial Internet of Things (IIoT) to enhance security and efficiency. This study introduces a Reinforcement Learning-based Resource Scheduling Approach for Blockchain Networks in IIoT. Unlike previous studies, which mainly focus on either blockchain security or resource allocation, our approach integrates reinforcement learning for dynamic resource scheduling, improving efficiency while minimizing latency. The methodology is illustrated through a flowchart. Simulation results validate the effectiveness in multiple scenarios. Future work includes enhancing inter-node communication reliability.
Graphical Abstract
Enhanced Reinforcement Learning-Based Resource Scheduling for Secure Blockchain Networks in IIoT
Keywords
fog computing
blockchain
measurement models
reinforcement-Learning
IIoT
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The author declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
References
  1. Chiang, M., & Zhang, T. (2016). Fog and IoT: An overview of research opportunities. IEEE Internet of Things Journal, 3(6), 854–864.
    [CrossRef]   [Google Scholar]
  2. Perera, T. D. P. (2018). Simultaneous wireless information and power transfer (SWIPT): Recent advances and future challenges. IEEE Communications Surveys & Tutorials, 20(1), 264–302.
    [CrossRef]   [Google Scholar]
  3. Zhou, C., Gu, Y., Zhang, Y. D., Shi, Z., Jin, T., & Wu, X. (2017). Compressive sensing-based coprime array direction-of-arrival estimation. IET Communications, 11(11), 1719-1724.
    [CrossRef]   [Google Scholar]
  4. Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R. H., Morrow, M. J., & Polakos, P. A. (2018). A comprehensive survey on fog computing: State-of-the-art and research challenges. IEEE Communications Surveys & Tutorials, 20(1), 416–464.
    [CrossRef]   [Google Scholar]
  5. Baidas, M. W., Alsusa, E., & Hamdi, K. A. (2020). Joint relay selection and power allocation for NOMA-based multicast cognitive radio networks. IET Communications, 14(13), 2027–2037.
    [CrossRef]   [Google Scholar]
  6. Mekala, M. S., & Viswanathan, P. (2020). A survey: energy-efficient sensor and VM selection approaches in green computing for X-IoT applications. International Journal of Computers and Applications, 42(3), 290–305.
    [CrossRef]   [Google Scholar]
  7. Men, J., & Ge, J. (2015). Performance analysis of non-orthogonal multiple access in downlink cooperative network. IET Communications, 9(18), 2267–2273.
    [CrossRef]   [Google Scholar]
  8. Xu, D., Li, Y., Chen, X., Li, J., Hui, P., Chen, S., & Crowcroft, J. (2018). A survey of opportunistic offloading. IEEE Communications Surveys & Tutorials, 20(3), 2198-2236.
    [CrossRef]   [Google Scholar]
  9. Chen, N., Yang, Y., Zhang, T., Zhou, M.-T., Luo, X., & Zao, J. K. (2018). Fog as a service technology. IEEE Communications Magazine, 56(11), 95–101.
    [CrossRef]   [Google Scholar]
  10. Kumar, C., & Kashyap, S. (2020). Massive MIMO enabled joint unicast transmission to IoT devices and mobile terminals. IET Communications, 14(16), 2048–2059.
    [CrossRef]   [Google Scholar]
  11. Mekala, M. S., & Viswanathan, P. (2019). Equilibrium transmission bilevel energy efficient node selection approach for Internet of Things. Wireless Personal Communications, 108(3), 1635-1663.
    [CrossRef]   [Google Scholar]
  12. Krikidis, I., Timotheou, S., Nikolaou, S., Zheng, G., Ng, D. W. K., & Schober, R. (2014). Simultaneous wireless information and power transfer in modern communication systems. IEEE Communications Magazine, 52(11), 104–110.
    [CrossRef]   [Google Scholar]
  13. Soundararajan, R., Palanisamy, N., Patan, R., Nagasubramanian, G., & Khan, M. S. (2020). Secure and concealed watchdog selection scheme using masked distributed selection approach in wireless sensor networks. IET Communications, 14(6), 948–955.
    [CrossRef]   [Google Scholar]
  14. You, C., Huang, K., Chae, H., & Kim, B.-H. (2017). Energy-efficient resource allocation for mobile-edge computation offloading. IEEE Transactions on Wireless Communications, 16(3), 1397–1411.
    [CrossRef]   [Google Scholar]
  15. Yang, Y., Wang, K., Zhang, G., Chen, X., Luo, X., & Zhou, M.-T. (2018). MEETS: Maximal energy efficient task scheduling in homogeneous fog networks. IEEE Internet of Things Journal, 5(5), 4076–4087.
    [CrossRef]   [Google Scholar]
  16. Paranjothi, A., Khan, M. S., Patan, R., Parizi, R. M., & Atiquzzaman, M. (2020). VANE-Tomo: A congestion identification and control scheme in connected vehicles using network tomography. Computer Communications, 151, 275–289.
    [CrossRef]   [Google Scholar]
  17. Yang, Y., Zhao, S., Zhang, W., Chen, Y., Luo, X., & Wang, J. (2018). DEBTS: Delay energy balanced task scheduling in homogeneous fog networks. IEEE Internet of Things Journal, 5(3), 2094–2106.
    [CrossRef]   [Google Scholar]
  18. Tran, T. X., & Pompili, D. (2019). Joint task offloading and resource allocation for multi-server mobile-edge computing networks. IEEE Transactions on Vehicular Technology, 68(1), 856–868.
    [CrossRef]   [Google Scholar]
  19. El Haber, E., Nguyen, T. M., & Assi, C. (2019). Joint optimization of computational cost and devices energy for task offloading in multi-tier edge-clouds. IEEE Transactions on Communications, 67(5), 3407–3421.
    [CrossRef]   [Google Scholar]
  20. Zheng, H. N., Xiong, K., Fan, P. Y., Zhou, L., & Zhong, Z. (2018). SWIPT-aware fog information processing: Local computing vs. fog offloading. Sensors, 18(10), 3291–3307.
    [CrossRef]   [Google Scholar]
  21. Zheng, H., Xiong, K., Fan, P., Zhong, Z., & Letaief, K. B. (2019). Fog-assisted multiuser SWIPT networks: Local computing or offloading. IEEE Internet of Things Journal, 6(3), 5246–5264.
    [CrossRef]   [Google Scholar]
  22. Mekala, M. S., & Viswanathan, P. (2019). Energy-efficient virtual machine selection based on resource ranking and utilization factor approach in cloud computing for IoT. Computers & Electrical Engineering, 73, 227-244.
    [CrossRef]   [Google Scholar]
  23. Zhou, A., Wang, S., Cheng, B., Zheng, Z., Yang, F., Chang, R. N., ... & Buyya, R. (2016). Cloud service reliability enhancement via virtual machine placement optimization. IEEE Transactions on Services Computing, 10(6), 902-913.
    [CrossRef]   [Google Scholar]
  24. Xu, H., Yang, B., Qi, W., & Ahene, E. (2016). A multi-objective optimization approach to workflow scheduling in clouds considering fault recovery. KSII Transactions on Internet and Information Systems, 10(3), 976–995.
    [CrossRef]   [Google Scholar]
  25. Kiani, A., & Ansari, N. (2017). Toward hierarchical mobile edge computing: An auction-based profit maximization approach. IEEE Internet of Things Journal, 4(6), 2082–2091.
    [CrossRef]   [Google Scholar]
  26. Fan, Q., & Ansari, N. (2018). Application aware workload allocation for edge computing based IoT. IEEE Internet of Things Journal, 5(3), 2146–2153.
    [CrossRef]   [Google Scholar]
  27. Rodrigues, T. G., Suto, K., Nishiyama, H., & Kato, N. (2016). Hybrid method for minimizing service delay in edge cloud computing through VM migration and transmission power control. IEEE Transactions on Computers, 66(5), 810-819.
    [CrossRef]   [Google Scholar]
  28. Yao, J., & Ansari, N. (2019). QoS-aware fog resource provisioning and mobile device power control in IoT networks. IEEE Transactions on Network and Service Management, 16(1), 167–175.
    [CrossRef]   [Google Scholar]
  29. Chen, K., & Wang, J. (2023). Scheduling Hybrid Spark Jobs Based on Deep Reinforcement Learning. In Proceedings of the 2nd International Conference on Big Data, Blockchain, and Economy Management (ICBBEM 2023). May 2023.
    [Google Scholar]
  30. Kiani, A., & Ansari, N. (2024). Hierarchical Adaptive Federated Reinforcement Learning for Efficient Resource Allocation in IoT Networks. Computer Communications, January 2024.
    [Google Scholar]
  31. Alipio, M., & Bures, M. (2023). Deep reinforcement learning perspectives on improving reliable transmissions in iot networks: Problem formulation, parameter choices, challenges, and future directions. Internet of Things, 23, 100846.
    [CrossRef]   [Google Scholar]
  32. Song, X., Liu, L., Fu, J., Zhang, X., Feng, J., & Pei, Q. (2023, December). A Reinforcement Learning-based DAG Tasks Scheduling in Edge-Cloud Collaboration Systems. In GLOBECOM 2023-2023 IEEE Global Communications Conference (pp. 1771-1776). IEEE.
    [CrossRef]   [Google Scholar]
  33. Boateng, G. O., Erbad, A., Seid, A. M., Hamdi, M., Guo, X., & Guizani, M. (2024, December). Coalitional Game-guided Reinforcement Learning for P2P Resource Trading in Sliced IIoT Networks. In GLOBECOM 2024-2024 IEEE Global Communications Conference (pp. 644-649). IEEE.
    [CrossRef]   [Google Scholar]
Cite This Article
APA Style
Garg, M. (2025). Enhanced Reinforcement Learning-Based Resource Scheduling for Secure Blockchain Networks in IIoT. IECE Transactions on Machine Intelligence, 1(1), 29–41. https://doi.org/10.62762/TMI.2024.529242
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