Cloud-Based Machine Learning in Real-Time Smart City Systems: Applications and Service Optimization

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Published: 2026-05-24
Keywords:
Cloud-Based Machine Learning, Smart City Systems, Real-Time Optimization, Service Scalability, Urban Analytics

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Haoran Gao
School of Computer Science, Henan University of Science and Technology, Luoyang, China
Feng Ding
School of Software Engineering, Jiangxi University of Science and Technology, Ganzhou, China
Jianguo Sun
School of Computer Science, Henan University of Science and Technology, Luoyang, China

Abstract

This research article explores the integration of cloud-based machine learning (ML) technologies into real-time smart city systems, emphasizing their applications and service optimization. The study begins by outlining the transformative potential of cloud-based ML in urban environments, followed by an analysis of existing methodologies and their limitations. A detailed explanation of the proposed framework is provided, including experimental setups and parameter configurations. Results demonstrate significant improvements in system efficiency, scalability, and predictive accuracy across various smart city applications, such as traffic management, energy optimization, and public safety. The discussion highlights the implications of these findings, addressing challenges such as latency, data security, and scalability. The article concludes with recommendations for future research and practical implementation strategies.

Article Details

Issue

Vol. 2 No. 1 (2026)

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Articles

How to Cite

Cloud-Based Machine Learning in Real-Time Smart City Systems: Applications and Service Optimization. (2026). Hua Xia Xin Zhi, 2(1), 195-206. https://journals.hubblepress.com/index.php/HXXZ/article/view/40

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