Enhanced Reinforcement Learning-Based Resource Scheduling for Secure Blockchain Networks in IIoT

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.