Artificial Intelligence and Data Science for Smart Cities

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1256

Special Issue Editor


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Guest Editor
Institute for Intelligent Systems, and Department of Electrical & Computer Engineering, University of Memphis, Memphis, TN 38152, USA
Interests: Artificial Intelligence; machine learning; data science; cognitive science

Special Issue Information

Dear Colleagues,

A smart city is a technologically advanced urban area that improves its residents’ quality of life via the optimal use of resources. Artificial Intelligence (AI) and data science play a pivotal role in the development and sustenance of smart cities. AI enables the efficient management and usage of vast amounts of data generated by various interconnected systems such as security, safety, healthcare, transportation, energy grids, water supply, and other public services. Predictive analytics and machine learning (ML) models can enhance urban planning, reduce traffic congestion, and improve resource allocation. Predictive AI/ML models can monitor real-time data from the Internet of Things (IoT), automating key services and enabling proactive action. For example, such models can reduce congestion by predicting traffic patterns, optimize energy consumption by proactively managing smart grids, enhance public safety by predicting crime hotspots using historical data, and proactively assist in disaster management by predicting environmental changes. Moreover, AI and data analytics in smart cities promote sustainability by improving waste management, reducing energy usage, and enhancing water distribution. Data-driven insights enable a city to implement smart infrastructure solutions that can adapt to changing demands, leading to smarter, more resilient cities. However, the widespread deployment of AI technologies in smart cities poses challenges such as data privacy concerns, cybersecurity risks, scalability issues as the city population grows, fault tolerance requirement due to massive interconnectivity, and equitable access to the benefits of smart city initiatives.

Dr. Bonny Banerjee
Guest Editor

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Keywords

  • Internet of Things/Internet of Everything
  • cybersecurity
  • Edge AI
  • predictive analytics
  • connected vehicle technologies
  • public safety
  • smart healthcare
  • smart governance
  • energy efficiency
  • urban sustainability

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Published Papers (2 papers)

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19 pages, 4290 KiB  
Article
Active Distribution Network Source–Network–Load–Storage Collaborative Interaction Considering Multiple Flexible and Controllable Resources
by Sheng Li, Tianyu Chen and Rui Ding
Information 2025, 16(4), 325; https://doi.org/10.3390/info16040325 - 19 Apr 2025
Viewed by 359
Abstract
In the context of rapid advancement of smart cities, a distribution network (DN) serving as the backbone of urban operations is a way to confront multifaceted challenges that demand innovative solutions. Central among these, it is imperative to optimize resource allocation and enhance [...] Read more.
In the context of rapid advancement of smart cities, a distribution network (DN) serving as the backbone of urban operations is a way to confront multifaceted challenges that demand innovative solutions. Central among these, it is imperative to optimize resource allocation and enhance the efficient utilization of diverse energy sources, with particular emphasis on seamless integration of renewable energy systems into existing infrastructure. At the same time, considering that the traditional power system’s “rigid”, instantaneous, dynamic, and balanced law of electricity, “source-load”, is difficult to adapt to the grid-connection of a high proportion of distributed generations (DGs), the collaborative interaction of multiple flexible controllable resources, like flexible loads, are able to supplement the power system with sufficient “flexibility” to effectively alleviate the uncertainty caused by intermittent fluctuations in new energy. Therefore, an active distribution network (ADN) intraday, reactive, power optimization-scheduling model is designed. The dynamic reactive power collaborative interaction model, considering the integration of DG, energy storage (ES), flexible loads, as well as reactive power compensators into the IEEE 33-node system, is constructed with the goals of reducing intraday network losses, keeping voltage deviations to a minimum throughout the day, and optimizing static voltage stability in an active distribution network. Simulation outcomes for an enhanced IEEE 33-node system show that coordinated operation of source–network–load–storage effectively reduces intraday active power loss, improves voltage regulation capability, and achieves secure and reliable operation under ADN. Therefore, it will contribute to the construction of future smart city power systems to a certain extent. Full article
(This article belongs to the Special Issue Artificial Intelligence and Data Science for Smart Cities)
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47 pages, 1040 KiB  
Systematic Review
Impact of EU Regulations on AI Adoption in Smart City Solutions: A Review of Regulatory Barriers, Technological Challenges, and Societal Benefits
by Bo Nørregaard Jørgensen and Zheng Grace Ma
Information 2025, 16(7), 568; https://doi.org/10.3390/info16070568 - 2 Jul 2025
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Abstract
This review investigates the influence of European Union regulations on the adoption of artificial intelligence in smart city solutions, with a structured emphasis on regulatory barriers, technological challenges, and societal benefits. It offers a comprehensive analysis of the legal frameworks in effect by [...] Read more.
This review investigates the influence of European Union regulations on the adoption of artificial intelligence in smart city solutions, with a structured emphasis on regulatory barriers, technological challenges, and societal benefits. It offers a comprehensive analysis of the legal frameworks in effect by 2025, including the Artificial Intelligence Act, General Data Protection Regulation, Data Act, and sector-specific directives governing mobility, energy, and surveillance. This study critically assesses how these regulations affect the deployment of AI systems across urban domains such as traffic optimization, public safety, waste management, and energy efficiency. A comparative analysis of regulatory environments in the United States and China reveals differing governance models and their implications for innovation, safety, citizen trust, and international competitiveness. The review concludes that although the European Union’s focus on ethics and accountability establishes a solid basis for trustworthy artificial intelligence, the complexity and associated compliance costs create substantial barriers to adoption. It offers recommendations for policymakers, municipal authorities, and technology developers to align regulatory compliance with effective innovation in the context of urban digital transformation. Full article
(This article belongs to the Special Issue Artificial Intelligence and Data Science for Smart Cities)
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