IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v14y2022i2p64-d754434.html
   My bibliography  Save this article

Digital Twin—Cyber Replica of Physical Things: Architecture, Applications and Future Research Directions

Author

Listed:
  • Cheng Qian

    (Department of Computer and Information Science, Towson University, Towson, MD 21252, USA)

  • Xing Liu

    (Department of Computer and Information Science, Towson University, Towson, MD 21252, USA)

  • Colin Ripley

    (Department of Computer and Information Science, Towson University, Towson, MD 21252, USA)

  • Mian Qian

    (Department of Computer and Information Science, Towson University, Towson, MD 21252, USA)

  • Fan Liang

    (Department of Computer Science, Sam Houston State University, Huntsville, TX 77340, USA)

  • Wei Yu

    (Department of Computer and Information Science, Towson University, Towson, MD 21252, USA)

Abstract

The Internet of Things (IoT) connects massive smart devices to collect big data and carry out the monitoring and control of numerous things in cyber-physical systems (CPS). By leveraging machine learning (ML) and deep learning (DL) techniques to analyze the collected data, physical systems can be monitored and controlled effectively. Along with the development of IoT and data analysis technologies, a number of CPS (smart grid, smart transportation, smart manufacturing, smart cities, etc.) adopt IoT and data analysis technologies to improve their performance and operations. Nonetheless, directly manipulating or updating the real system has inherent risks. Thus, creating a digital clone of a real physical system, denoted as a Digital Twin (DT), is a viable strategy. Generally speaking, a DT is a data-driven software and hardware emulation platform, which is a cyber replica of physical systems. Meanwhile, a DT describes a specific physical system and tends to achieve the functions and use cases of physical systems. Since DT is a complex digital system, finding a way to effectively represent a variety of things in timely and efficient manner poses numerous challenges to the networking, computing, and data analytics for IoT. Furthermore, the design of a DT for IoT systems must consider numerous exceptional requirements (e.g., latency, reliability, safety, scalability, security, and privacy). To address such challenges, the thoughtful design of DTs offers opportunities for novel and interdisciplinary research efforts. To address the aforementioned problems and issues, in this paper, we first review the architectures of DTs, data representation, and communication protocols. We then review existing efforts on applying DT into IoT data-driven smart systems, including the smart grid, smart transportation, smart manufacturing, and smart cities. Further, we summarize the existing challenges from CPS, data science, optimization, and security and privacy perspectives. Finally, we outline possible future research directions from the perspectives of performance, new DT-driven services, model and learning, and security and privacy.

Suggested Citation

  • Cheng Qian & Xing Liu & Colin Ripley & Mian Qian & Fan Liang & Wei Yu, 2022. "Digital Twin—Cyber Replica of Physical Things: Architecture, Applications and Future Research Directions," Future Internet, MDPI, vol. 14(2), pages 1-25, February.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:2:p:64-:d:754434
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/14/2/64/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/14/2/64/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kamil Židek & Ján Piteľ & Milan Adámek & Peter Lazorík & Alexander Hošovský, 2020. "Digital Twin of Experimental Smart Manufacturing Assembly System for Industry 4.0 Concept," Sustainability, MDPI, vol. 12(9), pages 1-16, May.
    2. Isaías González & Antonio José Calderón & José María Portalo, 2021. "Innovative Multi-Layered Architecture for Heterogeneous Automation and Monitoring Systems: Application Case of a Photovoltaic Smart Microgrid," Sustainability, MDPI, vol. 13(4), pages 1-24, February.
    3. Ehab Shahat & Chang T. Hyun & Chunho Yeom, 2021. "City Digital Twin Potentials: A Review and Research Agenda," Sustainability, MDPI, vol. 13(6), pages 1-20, March.
    4. Samer Jaloudi, 2019. "Communication Protocols of an Industrial Internet of Things Environment: A Comparative Study," Future Internet, MDPI, vol. 11(3), pages 1-18, March.
    5. Cagnano, A. & De Tuglie, E. & Mancarella, P., 2020. "Microgrids: Overview and guidelines for practical implementations and operation," Applied Energy, Elsevier, vol. 258(C).
    6. Vinoth Kumar Ponnusamy & Padmanathan Kasinathan & Rajvikram Madurai Elavarasan & Vinoth Ramanathan & Ranjith Kumar Anandan & Umashankar Subramaniam & Aritra Ghosh & Eklas Hossain, 2021. "A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid," Sustainability, MDPI, vol. 13(23), pages 1-35, December.
    7. Dileep, G., 2020. "A survey on smart grid technologies and applications," Renewable Energy, Elsevier, vol. 146(C), pages 2589-2625.
    8. Fei Tao & Fangyuan Sui & Ang Liu & Qinglin Qi & Meng Zhang & Boyang Song & Zirong Guo & Stephen C.-Y. Lu & A. Y. C. Nee, 2019. "Digital twin-driven product design framework," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3935-3953, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Feng, Hailin & Lv, Haibin & Lv, Zhihan, 2023. "Resilience towarded Digital Twins to improve the adaptability of transportation systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    2. Filippo Poltronieri & Cesare Stefanelli & Mauro Tortonesi & Mattia Zaccarini, 2023. "Reinforcement Learning vs. Computational Intelligence: Comparing Service Management Approaches for the Cloud Continuum," Future Internet, MDPI, vol. 15(11), pages 1-30, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Claire Daniel & Christopher Pettit, 2022. "Charting the past and possible futures of planning support systems: Results of a citation network analysis," Environment and Planning B, , vol. 49(7), pages 1875-1892, September.
    2. Matthew Gough & Sérgio F. Santos & Mohammed Javadi & Rui Castro & João P. S. Catalão, 2020. "Prosumer Flexibility: A Comprehensive State-of-the-Art Review and Scientometric Analysis," Energies, MDPI, vol. 13(11), pages 1-32, May.
    3. Vladislav Volnyi & Pavel Ilyushin & Konstantin Suslov & Sergey Filippov, 2023. "Approaches to Building AC and AC–DC Microgrids on Top of Existing Passive Distribution Networks," Energies, MDPI, vol. 16(15), pages 1-26, August.
    4. Issam A. R. Moghrabi & Sameer Ahmad Bhat & Piotr Szczuko & Rawan A. AlKhaled & Muneer Ahmad Dar, 2023. "Digital Transformation and Its Influence on Sustainable Manufacturing and Business Practices," Sustainability, MDPI, vol. 15(4), pages 1-35, February.
    5. Erdal Irmak & Ersan Kabalci & Yasin Kabalci, 2023. "Digital Transformation of Microgrids: A Review of Design, Operation, Optimization, and Cybersecurity," Energies, MDPI, vol. 16(12), pages 1-58, June.
    6. Muhammad Awais Shahid & Fiaz Ahmad & Fahad R. Albogamy & Ghulam Hafeez & Zahid Ullah, 2022. "Detection and Prevention of False Data Injection Attacks in the Measurement Infrastructure of Smart Grids," Sustainability, MDPI, vol. 14(11), pages 1-25, May.
    7. Sepideh Radhoush & Bradley M. Whitaker & Hashem Nehrir, 2023. "An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks," Energies, MDPI, vol. 16(16), pages 1-29, August.
    8. Amelio, Andrea & Giardino-Karlinger, Liliane & Valletti, Tommaso, 2020. "Exclusionary pricing in two-sided markets," International Journal of Industrial Organization, Elsevier, vol. 73(C).
    9. Jihed Hmad & Azeddine Houari & Allal El Moubarek Bouzid & Abdelhakim Saim & Hafedh Trabelsi, 2023. "A Review on Mode Transition Strategies between Grid-Connected and Standalone Operation of Voltage Source Inverters-Based Microgrids," Energies, MDPI, vol. 16(13), pages 1-41, June.
    10. Jimmy Gallegos & Paul Arévalo & Christian Montaleza & Francisco Jurado, 2024. "Sustainable Electrification—Advances and Challenges in Electrical-Distribution Networks: A Review," Sustainability, MDPI, vol. 16(2), pages 1-33, January.
    11. Antoine Boche & Clément Foucher & Luiz Fernando Lavado Villa, 2022. "Understanding Microgrid Sustainability: A Systemic and Comprehensive Review," Energies, MDPI, vol. 15(8), pages 1-29, April.
    12. Yukun Xu & Xiangyong Kong & Zheng Zhu & Chao Jiang & Shuang Xiao, 2022. "Recovery Algorithm of Power Metering Data Based on Collaborative Fitting," Energies, MDPI, vol. 15(4), pages 1-19, February.
    13. Ma, Shuaiyin & Ding, Wei & Liu, Yang & Ren, Shan & Yang, Haidong, 2022. "Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries," Applied Energy, Elsevier, vol. 326(C).
    14. Yen Sheng Tsai & Wei-Hsi Hung, 2023. "A low-cost intelligent tracking system for clothing manufacturers," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 473-491, February.
    15. Abdul K Hamid & Nsilulu T Mbungu & A. Elnady & Ramesh C Bansal & Ali A Ismail & Mohammad A AlShabi, 2023. "A systematic review of grid-connected photovoltaic and photovoltaic/thermal systems: Benefits, challenges and mitigation," Energy & Environment, , vol. 34(7), pages 2775-2814, November.
    16. Maurizio Bevilacqua & Eleonora Bottani & Filippo Emanuele Ciarapica & Francesco Costantino & Luciano Di Donato & Alessandra Ferraro & Giovanni Mazzuto & Andrea Monteriù & Giorgia Nardini & Marco Orten, 2020. "Digital Twin Reference Model Development to Prevent Operators’ Risk in Process Plants," Sustainability, MDPI, vol. 12(3), pages 1-17, February.
    17. Hafize Nurgul Durmus Senyapar & Ramazan Bayindir, 2023. "The Research Agenda on Smart Grids: Foresights for Social Acceptance," Energies, MDPI, vol. 16(18), pages 1-31, September.
    18. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    19. Faisal Mumtaz & Kashif Imran & Abdullah Abusorrah & Syed Basit Ali Bukhari, 2022. "Harmonic Content-Based Protection Method for Microgrids via 1-Dimensional Recursive Median Filtering Algorithm," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    20. Ruben Hidalgo-Leon & Fernando Amoroso & Javier Urquizo & Viviana Villavicencio & Miguel Torres & Pritpal Singh & Guillermo Soriano, 2022. "Feasibility Study for Off-Grid Hybrid Power Systems Considering an Energy Efficiency Initiative for an Island in Ecuador," Energies, MDPI, vol. 15(5), pages 1-25, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:14:y:2022:i:2:p:64-:d:754434. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.