RLChain: A DRL Approach for Blockchain Performance Optimization Toward IIoT

With the development of communication technology and Internet of Things, Industrial Internet of Things (IIoT) is proposed in the automation industry for complex scenarios. Blockchain is applied in IIoT to solve data security and privacy issues related to centralized data storage and processing. However, there are inevitably performance issues with throughput constraints when blockchain manages large amounts of device data. This paper proposes a blockchain-supported performance optimization framework for IIoT systems using deep reinforcement learning (DRL) methods. We model the blockchain performance optimization problem as a Markov decision process that optimizes the blockchain’s throughput by dynamically adjusting the block size and interval through DRL while satisfying security constraints. We use the double deep Q-network (DDQN) to deal with the dynamic and complexity of optimization problems due to the heterogeneity of equipment and diversified requirements. We also alleviate the overestimation problem caused by DQN. Meanwhile, we study the impact of the number of network layers and different activation units on the performance optimization method in DDQN. Finally, we prove that our work is feasible and effective through the case study based on actual IIoT scenario datasets. Experimental results demonstrate that our proposed scheme enhances blockchain performance in IIoT systems. The detailed qualitative comparison with related work demonstrates the superiority and innovation of our work and proves that it improves the shortcomings of existing work.