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.

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IEEE Transactions on Industrial Informatics
School of Information Technology

Liu, M. (Mengting), Yu, F.R, Teng, Y. (Yinglei), Leung, V.C.M. (Victor C. M.), & Song, M. (Mei). (2019). Performance optimization for blockchain-enabled industrial internet of things (iiot) systems: A deep reinforcement learning approach. IEEE Transactions on Industrial Informatics, 15(6), 3559–3570. doi:10.1109/TII.2019.2897805