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Title	Realizing Sustainable And Adaptive Smart Cities With Ai-Powered Digital Twin
ID_Doc	44514
Authors	Chen N.; Ye Q.J.; Rashid M.; Zheng Y.
Year	2025
Published	IEEE Internet of Things Magazine
DOI	http://dx.doi.org/10.1109/MIOT.2025.3575884
Abstract	Smart cities incorporating smart grid and intelligent transportation systems are an emerging paradigm that aims to improve the quality of life for residents through ubiquitous networking coverage, automation, and artificial intelligence (AI). However, the emergence of electric and autonomous vehicles (EAVs), the wide expansion of data centers, and the high integration of renewable energy impose heavy burdens on the ossified smart cities infrastructure. In this article, we develop a sustainable and adaptive digital twin (DT) framework to characterize smart cities with high fidelity and real-time synchronization between physical and digital spaces of smart city applications. Different from existing DT models, the developed framework considers the interconnections among multiple subsystems, where each subsystem is connected with real-world physical space to achieve real-time interaction. Considering large-scale system simulations and huge amounts of Internet-of-Things (IoT) data to be processed, we propose a hierarchical computing architecture and a DT update mechanism to decompose and distribute computing tasks according to the capabilities of data centers and local computing devices. Two case studies are presented to validate the adaptability and high fidelity of the developed DT framework that achieves close-to-optimal performance with partially lacking data. © 2018 IEEE.
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