An Overlapping Self-Organizing Sharding Scheme Based on DRL for Large-Scale IIoT Blockchain

Xinjie Yang, Tianyi Xu, Fengbiao Zan, Tao Ye, Zhaofang Mao, Tie Qiu

Abstract: Sharding is widely regarded as a highly promising solution to address the scalability limitations of blockchain. However, the scalability and throughput improved by using sharding are limited by the verification of cross-shard transactions. To reduce cross-shard transaction and improve the throughput of the blockchain, the existing sharding schemes are based on factors such as the edge-end structure of Industrial Internet of Things (IIoT) for sharding. But these schemes are centralized, leading to the problems of low sharding efficiency, poor scalability, and poor security. Moreover, these schemes adopt a nonoverlapping sharding architecture, so the verification cost of cross-shard transactions is significantly higher than that of intrashard transactions. In order to solve the above problems, this article proposes an overlapping self-organizing sharding scheme (deep reinforcement learning (DRL)-OSS) for large-scale IIoT blockchain. Based on local blockchain information, such as nodes’ information and transaction interaction frequency, DRL-OSS uses DRL to achieve self-organizing sharding with the aim to maximize the throughput and security of blockchain. In addition, based on the threat model, this article also designs a block complaint scheme (BCS) to further improve the security of the blockchain, thereby avoiding the reduction in resistance to 1% attack due to the poor anti-predictability of shards and the dilution of computing power. Through experimental verification and analysis, DRL-OSS improves the throughput by 50% when compared to state-of-the-art sharding schemes and has higher system security.