Future Smart Cities As Cyber-Physical Systems: Economic Challenges and Opportunities

Elahe Taherianfard, Mohammad Hossein Heydari, Taher Niknam, Aliasghar Baziar and Mohammadreza Askari

MPRA Paper from University Library of Munich, Germany

Abstract: This study explores the integration of Variational Autoencoders (VAEs) and Genetic Programming (GP) to address key challenges in the development of smart cities as cyber-physical systems (CPS). The primary objective is to enhance decision-making processes, optimize resource allocation, and improve energy management within urban infrastructures. VAEs are employed for dimensionality reduction and feature extraction, enabling efficient processing of large-scale urban data, while GP is utilized for optimization, ensuring the effective configuration and management of smart city systems. The proposed framework is evaluated across various metrics, including energy consumption, system resilience, and traffic flow optimization. The results demonstrate substantial improvements over traditional methods, highlighting the potential of the VAEs + GP combination in tackling complex CPS challenges. This approach not only contributes to the advancement of smart city technologies but also offers a scalable and adaptive solution to the evolving demands of urban environments. Overall, the study showcases the transformative potential of combining deep learning and evolutionary algorithms to build sustainable and intelligent smart cities.

Keywords: Smart Cities; Cyber-Physical Systems (CPS); Variational Autoencoders (VAEs); Genetic Programming (GP); Resource Allocation; Energy Management; Dimensionality Reduction; Optimization Algorithms; Urban Data Processing; Intelligent Systems (search for similar items in EconPapers)
JEL-codes: Q0 Q4 Q41 R0 (search for similar items in EconPapers)
Date: 2024-12
New Economics Papers: this item is included in nep-ene
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