DS-RAM: A dynamic sharding and reputation-based auditing mechanisms for blockchain consensus in IIoT

Abstract

Sharding is an effective strategy to improve the scalability of blockchain, especially in the context of massive data processing in Industrial Internet of Things (IIoT) scenarios. However, existing sharding schemes often overlook factors such as node reputation, resource capacity, and historical behavior, leading to imbalanced resource allocation, which in turn causes delays in real-time data processing and compromises system security. The blockchain consensus mechanism determines how nodes reach consensus, serving as the core of system efficiency and security. However, traditional consensus mechanisms lack effective detection of malicious nodes and insufficient supervision of consensus nodes, making the system vulnerable to attacks and malicious actions. To address these issues, this paper proposes DS-RAM (Dynamic Sharding and Reputation-based Auditing Mechanism), a dynamic sharding mechanism based on the weighted K-Medoids and Canopy algorithms. It comprehensively considers factors such as node geographical location, reputation, interaction frequency, and historical behavior to optimize node allocation, ensuring balanced distribution of sharding resources, thus improving system throughput and security. Additionally, DS-RAM introduces an auditing node module, which provides additional supervision of consensus nodes based on the reputation mechanism, enabling timely detection and isolation of potential malicious nodes, thereby effectively enhancing the fault tolerance of the consensus mechanism and system security. Simulation results demonstrate that, compared to traditional sharding schemes and reputation-based blockchains, the proposed method can effectively improve sharding security and blockchain sharding performance.