Advancing Smart City Sustainability Through Artificial Intelligence, Digital Twin and Blockchain Solutions
This paper presents an integrated Smart City platform that combines digital twin technology, advanced machine learning, and a private blockchain network to enhance data-driven decision making and operational efficiency in both public enterprises and small and medium-sized enterprises (SMEs). The pro...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Technologies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7080/13/7/300 |
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| Summary: | This paper presents an integrated Smart City platform that combines digital twin technology, advanced machine learning, and a private blockchain network to enhance data-driven decision making and operational efficiency in both public enterprises and small and medium-sized enterprises (SMEs). The proposed cloud-based business intelligence model automates Extract, Transform, Load (ETL) processes, enables real-time analytics, and secures data integrity and transparency through blockchain-enabled audit trails. By implementing the proposed solution, Smart City and public service providers can significantly improve operational efficiency, including a 15% reduction in costs and a 12% decrease in fuel consumption for waste management, as well as increased citizen engagement and transparency in Smart City governance. The digital twin component facilitated scenario simulations and proactive resource management, while the participatory governance module empowered citizens through transparent, immutable records of proposals and voting. This study also discusses technical, organizational, and regulatory challenges, such as data integration, scalability, and privacy compliance. The results indicate that the proposed approach offers a scalable and sustainable model for Smart City transformation, fostering citizen trust, regulatory compliance, and measurable environmental and social benefits. |
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| ISSN: | 2227-7080 |