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dc.contributor.authorSahal, Radhya
dc.contributor.authorAlsamhi, Saeed H.
dc.contributor.authorBrown, Kenneth N.
dc.contributor.authorO'Shea, Donna
dc.contributor.authorMcCarthy, Connor
dc.contributor.authorGuizani, Mohsen
dc.date.accessioned2021-10-14T11:31:11Z
dc.date.available2021-10-14T11:31:11Z
dc.date.copyright2021
dc.date.issued2021-09-09
dc.identifier.citationSahal, R.; Alsamhi, S.H.; Brown, K.N.; O’Shea, D.; McCarthy, C.; Guizani, M. 2021. Blockchain-Empowered Digital Twins Collaboration: Smart Transportation Use Case. Machines, 9, 193. https://doi.org/10.3390/machines9090193en_US
dc.identifier.urihttp://research.thea.ie/handle/20.500.12065/3717
dc.description.abstractDigital 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 areaen_US
dc.formatPDFen_US
dc.language.isoengen_US
dc.relation.ispartofMachinesen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectBlockchainen_US
dc.subjectDigital twinsen_US
dc.subjectIndustry 4.0en_US
dc.subjectSmart manufacturingen_US
dc.subjectData analysisen_US
dc.subjectTransportationen_US
dc.subjectLogisticsen_US
dc.subjectRailwayen_US
dc.titleBlockchain-empowered digital twins collaboration: smart transportation use case.en_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.contributor.affiliationAthlone Institute of Technologyen_US
dc.contributor.sponsorScience Foundation Ireland (SFI) under Grant Number SFI/16/RC/3918 (CONFIRM), and Marie Skłodowska- Curie grant agreement No. 847577 co-funded by the European Regional Development Fund.en_US
dc.description.peerreviewyesen_US
dc.identifier.doi10.3390/machines9090193en_US
dc.identifier.eissn2075-1702
dc.identifier.orcidhttps://orcid.org/ 0000-0003-2857-6979en_US
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessen_US
dc.subject.departmentSoftware Research Institute AITen_US
dc.type.versioninfo:eu-repo/semantics/publishedVersionen_US


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International