A Survey on Digital Twin for Industrial Internet of Things: Applications, Technologies and Tools

【Author】 Xu, Hansong; Wu, Jun; Pan, Qianqian; Guan, Xinping; Guizani, Mohsen

【Source】IEEE COMMUNICATIONS SURVEYS AND TUTORIALS

【影响因子】33.840

【Abstract】Digital twin for the industrial Internet of Things (DT-IIoT) creates a high-fidelity, fine-grained, low-cost digital replica of the cyber-physical integrated Internet for industry. Powered by artificial intelligence (AI) and security technologies, DT-IIoT provides advanced features such as real-time monitoring, predictive maintenance, remote diagnostics, and rapid response for smart IIoT systems. A systematic review of key enabling technologies such as digital twin, AI, and blockchain is essential to develop DT-IIoT and reveal pitfalls. This paper reviews the preliminaries, real-world applications, architectures and models of digital twin-driven IIoT. In addition, advanced technologies for intelligent and secure DT-IIoT are investigated, including state-of-the-art AI solutions such as transfer learning and federated learning, as well as blockchain-based security solutions. Moreover, software tools for high-fidelity digital twin modeling are proposed. A case study on reinforcement learning-based integrated-control, communication, and computing (3C) design is developed to demonstrate the AI-driven intelligent DT-IIoT. Finally, this paper outlines the prospective applications, challenges, and integrations with ABCDE (i.e., AI, Blockchain, cloud computing, big data, edge computing) as the future directions.

【Keywords】Digital twin; industrial Internet of Things; artificial intelligence; blockchain; integrated design

【发表时间】2023 OCT-DEC

【收录时间】2024-01-05

【文献类型】综述

【主题类别】

区块链技术-协同技术-数字孪生