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Toward Intelligent AIoT: A Comprehensive Survey on Digital Twin and Multimodal Generative AI Integration

Author

Listed:
  • Xiaoyi Luo

    (Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
    These authors contributed equally to this work.)

  • Aiwen Wang

    (Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
    These authors contributed equally to this work.)

  • Xinling Zhang

    (Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
    These authors also contributed equally to this work.)

  • Kunda Huang

    (Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
    These authors also contributed equally to this work.)

  • Songyu Wang

    (Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
    These authors also contributed equally to this work.)

  • Lixin Chen

    (Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China)

  • Yejia Cui

    (Department of Clinical Laboratory, The Affiliated Dongguan Songshan Lake Central Hospital, Guangdong Medical University, Dongguan 523326, China)

Abstract

The Artificial Intelligence of Things (AIoT) is rapidly evolving from basic connectivity to intelligent perception, reasoning, and decision making across domains such as healthcare, manufacturing, transportation, and smart cities. Multimodal generative AI (GAI) and digital twins (DTs) provide complementary solutions. DTs deliver high-fidelity virtual replicas for real-time monitoring, simulation, and optimization with GAI enhancing cognition, cross-modal understanding, and the generation of synthetic data. This survey presents a comprehensive overview of DT–GAI integration in the AIoT. We review the foundations of DTs and multimodal GAI and highlight their complementary roles. We further introduce the Sense–Map–Generate–Act (SMGA) framework, illustrating their interaction through the SMGA loop. We discuss key enabling technologies, including multimodal data fusion, dynamic DT evolution, and cloud–edge–end collaboration. Representative application scenarios, including smart manufacturing, smart cities, autonomous driving, and healthcare, are examined to demonstrate their practical impact. Finally, we outline open challenges, including efficiency, reliability, privacy, and standardization, and we provide directions for future research toward sustainable, trustworthy, and intelligent AIoT systems.

Suggested Citation

  • Xiaoyi Luo & Aiwen Wang & Xinling Zhang & Kunda Huang & Songyu Wang & Lixin Chen & Yejia Cui, 2025. "Toward Intelligent AIoT: A Comprehensive Survey on Digital Twin and Multimodal Generative AI Integration," Mathematics, MDPI, vol. 13(21), pages 1-44, October.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:21:p:3382-:d:1778366
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    References listed on IDEAS

    as
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    3. Sebastian Farquhar & Jannik Kossen & Lorenz Kuhn & Yarin Gal, 2024. "Detecting hallucinations in large language models using semantic entropy," Nature, Nature, vol. 630(8017), pages 625-630, June.
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