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    Blockchain-empowered digital twins collaboration: Smart transportation use case

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    Blockchain-empowered digital twins collaboration Smart transportation use case.pdf (1.526Mb)
    Date
    2021-09-01
    Author
    Sahal, Radhya
    Alsamhi, Saeed H.
    Brown, Kenneth N.
    O’shea, Donna
    McCarthy, Conor
    Guizani, Mohsen
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    Abstract
    Digital twins (DTs) is a promising technology in the revolution of the industry and essential for Industry 4.0. DTs play a vital role in improving distributed manufacturing, providing up-to-date operational data representation of physical assets, supporting decision-making, and avoiding the potential risks in distributed manufacturing systems. Furthermore, DTs need to collaborate within distributed manufacturing systems to predict the risks and reach consensus-based decision-making. However, DTs collaboration suffers from single failure due to attack and connection in a centralized manner, data interoperability, authentication, and scalability. To overcome the above challenges, we have discussed the major high-level requirements for the DTs collaboration. Then, we have proposed a conceptual framework to fulfill the DTs collaboration requirements by using the combination of blockchain, predictive analysis techniques, and DTs technologies. The proposed framework aims to empower more intelligence DTs based on blockchain technology. In particular, we propose a concrete ledger-based collaborative DTs framework that focuses on real-time operational data analytics and distributed consensus algorithms. Furthermore, we describe how the conceptual framework can be applied using smart transportation system use cases, i.e., smart logistics and railway predictive maintenance. Finally, we highlighted the future direction to guide interested researchers in this interesting area.
    URI
    https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85115127791&origin=inward
    DOI/handle
    http://dx.doi.org/10.3390/machines9090193
    http://hdl.handle.net/10576/35592
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    • Computer Science & Engineering [‎2428‎ items ]

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