题名 | Federated Digital Twin |
作者 | |
DOI | |
发表日期 | 2023
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ISSN | 1550-6525
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ISBN | 979-8-3503-3785-3
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会议录名称 | |
页码 | 115-116
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会议日期 | 4-5 Oct. 2023
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会议地点 | Singapore, Singapore
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摘要 | Digital Twin (DT) is a virtual replica of a physical system that is constantly receiving information from different data sources, enhancing its operations and processes through data analytics, predictions and simulations. The development of DTs relies on advancements in cutting-edge technologies namely IoT, Big data, Cloud computing and Artificial Intelligence; and although it was initially conceived in manufacturing, it is currently contributing to the digital transformation of several fields including aeronautics, healthcare, urban planning and agriculture. The existing body of research suggests that it will be expanded in the next few years with the implementation of sophisticated applications, therefore different proposals to achieve collective work between DTs have been investigated. Nevertheless, much research is needed to develop and validate appropriate mechanisms to ensure its successful deployment in complex real-world cases that require collaboration among individual systems. A Federated Digital Twin (FDT) has been identified as a promising solution for this approach, since it allows the interconnection among autonomous DTs in the virtual space, leveraging their advantages and enabling interaction, collaboration and shared learning. Additionally, since a FDT is envisaged as a network of cooperative DTs, cognitive principles can be applied to assist the overall operations through knowledge acquisition and reasoning, leading to an informed and intelligent decision making. This study aims to expand the FDT concept, develop mechanisms for coordination and synchronization based on well-defined FDT goals and connectionism theory. Furthermore, four architectural styles are provided to enable the integration of collaborative DTs within a federated environment, aiming to improve the operations in complex real-world systems. |
关键词 | |
学校署名 | 其他
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相关链接 | [IEEE记录] |
收录类别 | |
EI入藏号 | 20235015196071
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EI主题词 | Cloud analytics
; Complex networks
; Computation theory
; Data Analytics
; Decision making
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EI分类号 | Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1
; Computer Systems and Equipment:722
; Digital Computers and Systems:722.4
; Data Processing and Image Processing:723.2
; Artificial Intelligence:723.4
; Management:912.2
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来源库 | IEEE
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全文链接 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10305745 |
引用统计 | |
成果类型 | 会议论文 |
条目标识符 | http://sustech.caswiz.com/handle/2SGJ60CL/609953 |
专题 | 南方科技大学 |
作者单位 | 1.School of Computer Science, University of Birmingham, Birmingham, UK 2.Dept. of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China 3.Dept. of Informatics and Telecommunications, University of Thessaly, Lamia, Greece |
推荐引用方式 GB/T 7714 |
Christian Vergara,Rami Bahsoon,Georgios Theodoropoulos,et al. Federated Digital Twin[C],2023:115-116.
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条目包含的文件 | 条目无相关文件。 |
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