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The development of smart cities and smart campuses is changing as a result of the combination of Artificial Intelligence (AI), Digital Twins (DT), and the Internet of Things (IoT), which allows for improved automation, lossless interoperability, and intelligent decision-making. In this paper, we propose a framework to support the application of DT technology in the context of smart cities. Node-RED is the main hub for managing IoT sensor data, real-time monitoring, and actuator control in the smart city-oriented system presented in this study. As a hypervision dashboard, ThingBoard is incorporated into the suggested design to guarantee the smooth integration of diverse IoT devices. Furthermore, natural language-based interactions are made possible by Large Language Models (LLMs), such as ChatGPT-4, which allow AI-driven automation through text and voice instructions via a Telegram bot. Additionally, a Digital Twin built with Unity 3D is created to simulate and visualize urban infrastructure in real-time. The paper also discusses future enhancements, including predictive analytics, machine learning-driven optimization, and edge AI deployment, to further enhance the scalability and adaptability of smart city solutions.

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A Digital Twins Framework for Managing Building Environmental Parameters

  • 会议论文

  • 发表日期：

    2026年

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  • Sidi Mohammed Yelles Chaouche,
  • Samuel Gomes,
  • Sebti Foufou,
  • Sihao Deng,
  • Rui Wang

摘要

The development of smart cities and smart campuses is changing as a result of the combination of Artificial Intelligence (AI), Digital Twins (DT), and the Internet of Things (IoT), which allows for improved automation, lossless interoperability, and intelligent decision-making. In this paper, we propose a framework to support the application of DT technology in the context of smart cities. Node-RED is the main hub for managing IoT sensor data, real-time monitoring, and actuator control in the smart city-oriented system presented in this study. As a hypervision dashboard, ThingBoard is incorporated into the suggested design to guarantee the smooth integration of diverse IoT devices. Furthermore, natural language-based interactions are made possible by Large Language Models (LLMs), such as ChatGPT-4, which allow AI-driven automation through text and voice instructions via a Telegram bot. Additionally, a Digital Twin built with Unity 3D is created to simulate and visualize urban infrastructure in real-time. The paper also discusses future enhancements, including predictive analytics, machine learning-driven optimization, and edge AI deployment, to further enhance the scalability and adaptability of smart city solutions.