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.

中文翻译：

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启用区块链的工业物联网（IIoT）系统的性能优化：一种深度强化学习方法

工业物联网（IIoT）的最新进展为各个行业提供了大量机会。为了解决海量IIoT数据的安全性和效率问题，区块链被广泛认为是一种以安全有效的方式实现数据存储/处理/共享的有前途的解决方案。为了满足高吞吐量需求，本文针对基于区块链的IIoT系统提出了一种基于深度强化学习（DRL）的新型性能优化框架，其目标包括三个方面：1）从以下方面提供一种评估系统的方法：可扩展性，分散性，延迟和安全性；2）在不影响系统的分散性，延迟和安全性的情况下，改善基础区块链的可扩展性；和3）为IIoT系统设计可调节的区块链，可以使用DRL技术选择/调整块生产者，共识算法，块大小和块间隔。仿真结果表明，我们提出的框架可以有效地提高支持区块链的IIoT系统的性能，并很好地适应IIoT的动态变化。