TY  - JOUR
AU  - Garg, Meenakshi
PY  - 2025
DA  - 2025/05/23
TI  - Enhanced Reinforcement Learning-Based Resource Scheduling for Secure Blockchain Networks in IIoT
JO  - ICCK Transactions on Machine Intelligence
T2  - ICCK Transactions on Machine Intelligence
JF  - ICCK Transactions on Machine Intelligence
VL  - 1
IS  - 1
SP  - 29
EP  - 41
DO  - 10.62762/TMI.2024.529242
UR  - https://www.icck.org/article/abs/TMI.2024.529242
KW  - fog computing
KW  - blockchain
KW  - measurement models
KW  - reinforcement learning
KW  - IIoT
AB  - 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.
SN  - 3068-7403
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Garg2025Enhanced,
  author = {Meenakshi Garg},
  title = {Enhanced Reinforcement Learning-Based Resource Scheduling for Secure Blockchain Networks in IIoT},
  journal = {ICCK Transactions on Machine Intelligence},
  year = {2025},
  volume = {1},
  number = {1},
  pages = {29-41},
  doi = {10.62762/TMI.2024.529242},
  url = {https://www.icck.org/article/abs/TMI.2024.529242},
  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.},
  keywords = {fog computing, blockchain, measurement models, reinforcement learning, IIoT},
  issn = {3068-7403},
  publisher = {Institute of Central Computation and Knowledge}
}