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2024 (English)In: Electronics, E-ISSN 2079-9292, Vol. 13, no 24, article id 4874Article, review/survey (Refereed) Published
Abstract [en]
Rapid urbanisation has intensified the need for sustainable solutions to address challenges in urban infrastructure, climate change, and resource constraints. This study reveals that Artificial Intelligence (AI)-enabled metaverse offers transformative potential for developing sustainable smart cities. AI techniques, such as machine learning, deep learning, generative AI (GAI), and large language models (LLMs), enhance the metaverse’s capabilities in data analysis, urban decision making, and personalised user experiences. The study further examines how these advanced AI models facilitate key metaverse technologies such as big data analytics, natural language processing (NLP), computer vision, digital twins, Internet of Things (IoT), Edge AI, and 5G/6G networks. Applications across various smart city domains—environment, mobility, energy, health, governance, and economy, and real-world use cases of virtual cities like Singapore, Seoul, and Lisbon are presented, demonstrating AI’s effectiveness in the metaverse for smart cities. However, AI-enabled metaverse in smart cities presents challenges related to data acquisition and management, privacy, security, interoperability, scalability, and ethical considerations. These challenges’ societal and technological implications are discussed, highlighting the need for robust data governance frameworks and AI ethics guidelines. Future directions emphasise advancing AI model architectures and algorithms, enhancing privacy and security measures, promoting ethical AI practices, addressing performance measures, and fostering stakeholder collaboration. By addressing these challenges, the full potential of AI-enabled metaverse can be harnessed to enhance sustainability, adaptability, and livability in smart cities.
Place, publisher, year, edition, pages
MDPI, 2024
Keywords
adaptive urban systems, artificial intelligence, digital twins, generative AI, large language models, metaverse, smart cities, sustainable cities, urban planning, urban transformation
National Category
Computer Systems Computer Engineering
Identifiers
urn:nbn:se:bth-27371 (URN)10.3390/electronics13244874 (DOI)001386778700001 ()2-s2.0-85213202921 (Scopus ID)
2025-01-102025-01-102025-01-10Bibliographically approved