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Towards a Domain-Neutral Platform for Sustainable Digital Twin Development

Author

Listed:
  • Goran Savić

    (Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia)

  • Milan Segedinac

    (Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia)

  • Zora Konjović

    (The Center of the Singidunum University in Novi Sad, Singidunum University Belgrade, 21000 Novi Sad, Serbia)

  • Milan Vidaković

    (Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia)

  • Radoslav Dutina

    (Rationale Novi Sad, 21000 Novi Sad, Serbia)

Abstract

In this paper, we propose an abstract domain-neutral architecture for a cognitive digital twin (CDT) and a software platform to develop such CDTs, including machine reasoning capabilities. Sustainable development refers here to an abstract model that enables a holistic view of limiting resources and has an ability to adapt to different application domains while reusing existing resources. The proposed solution allows for a unified abstract representation and the development of a wide range of diverse digital twins, as well as facilitating their interoperability. The abstract architecture consists of a four-layer structure (observation/actuation layer, data management layer, reasoning layer, and simulation layer) with an upper ontology to which the domain ontology of the specific CDT is mapped. The architecture relies on semantic web technologies, including ontology-based reasoning using OWL, and a loosely coupled, component-based service-oriented software architecture. The platform utilizes a microservice architecture that enables separate, loosely coupled services on each layer, message queues to provide asynchronous communication, and possesses cloud technologies to achieve scalability. The proposed approach was validated by implementing a software platform prototype and demonstrating its key features through two dissimilar scenarios. The first scenario demonstrates simple sustainable energy management through IoT systems inside smart buildings, while the second one demonstrates knowledge quality management based on knowledge space theory.

Suggested Citation

  • Goran Savić & Milan Segedinac & Zora Konjović & Milan Vidaković & Radoslav Dutina, 2023. "Towards a Domain-Neutral Platform for Sustainable Digital Twin Development," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13612-:d:1238034
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    References listed on IDEAS

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    1. Agustín Zaballos & Alan Briones & Alba Massa & Pol Centelles & Víctor Caballero, 2020. "A Smart Campus’ Digital Twin for Sustainable Comfort Monitoring," Sustainability, MDPI, vol. 12(21), pages 1-33, November.
    2. Sargin, Anatol & Ünlü, Ali, 2009. "Inductive item tree analysis: Corrections, improvements, and comparisons," Mathematical Social Sciences, Elsevier, vol. 58(3), pages 376-392, November.
    3. Vivek Warke & Satish Kumar & Arunkumar Bongale & Ketan Kotecha, 2021. "Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis," Sustainability, MDPI, vol. 13(18), pages 1-49, September.
    4. Odey Alshboul & Ali Shehadeh & Rabia Emhamed Al Mamlook & Ghassan Almasabha & Ali Saeed Almuflih & Saleh Y. Alghamdi, 2022. "Prediction Liquidated Damages via Ensemble Machine Learning Model: Towards Sustainable Highway Construction Projects," Sustainability, MDPI, vol. 14(15), pages 1-23, July.
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    Keywords

    CDT; ontology; OWL; microservice; IoT; KST;
    All these keywords.

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