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

被引:5
作者
Liu, Peng [1 ,2 ]
Xian, Youquan [1 ,2 ]
Yao, Chuanjian [1 ,2 ]
Wang, Peng [1 ,2 ]
Wang, Li-e [1 ,2 ]
Li, Xianxian [1 ,2 ]
机构
[1] Guangxi Normal Univ, Key Lab Educ Blockchain & Intelligent Technol, Minist Educ, Guilin 541004, Peoples R China
[2] Guangxi Normal Univ, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China
来源
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT | 2024年 / 21卷 / 05期
基金
中国国家自然科学基金;
关键词
Blockchains; Security; Industrial Internet of Things; Contracts; Quality of service; Task analysis; Soft sensors; IIoT; blockchain; oracle;
D O I
10.1109/TNSM.2024.3399837
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:5135 / 5148
页数:14
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