A Trustworthy and Consistent Blockchain Oracle Scheme for Industrial Internet of Things

• Published in: IEEE eTransactions on network and service management 2024-10, Vol.21 (5), p.5135-5148
• Main Authors: Liu, Peng, Xian, Youquan, Yao, Chuanjian, Wang, Peng, Wang, Li-e, Li, Xianxian
• Format: Article
• Language: English
• Subjects: Algorithms; Blockchain; Blockchains; Contracts; Cybersecurity; Data collection; IIoT; Industrial applications; Industrial Internet of Things; Internet of Things; Nodes; oracle; Quality of service; Quality of service architectures; Security; Soft sensors; Task analysis
• ISSN: 1932-4537; 1932-4537
• DOI: 10.1109/TNSM.2024.3399837

A blockchain provides decentralization and trustlessness features for the Industrial Internet of Things (IIoT), which expands the application scenarios of IIoT. To address the problem that blockchains cannot actively obtain off-chain data, the blockchain oracle is proposed as a bridge between the blockchain and external data. However, the existing oracle schemes make it difficult to solve the problem of low quality of service caused by frequent data changes and heterogeneous devices in IIoT, and the current oracle node selection schemes are difficult to balance security and quality of service. To tackle these problems, this paper proposes a secure and reliable oracle scheme that can obtain high-quality off-chain data. Specifically, we first design an oracle node selection algorithm based on a Verifiable Random Function (VRF) and reputation mechanism to securely select high-quality nodes. Second, we propose a data filtering algorithm based on a sliding window to further improve the consistency of the collected data. We verify the security of the proposed scheme through security analysis. The experimental results show that the proposed scheme can effectively select high-quality nodes, reduce data differences, and improve the quality of service of the oracle. In the oracle network with malicious nodes accounting for 10%, the data accuracy rate is increased by about 4%, and the data variance is reduced by about 45% on average.