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Smart Cities
Special Issues
Big Data and AI Services for Sustainable Smart Cities
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Big Data and AI Services for Sustainable Smart Cities

A special issue of Smart Cities (ISSN 2624-6511).

Deadline for manuscript submissions: 31 May 2025 | Viewed by 5632

Special Issue Editors

Prof. Dr. Jerry Gao

E-Mail Website1 Website2
Guest Editor
Computer Engineering Department and Applied Data Analytics Department, San Jose State University, One Washington Square, San Jose, CA 95192-0180, USA
Interests: smart cities; AI and machine learning; big data analytics; software engineering and test automation; AI testing
Dr. Fanjing Meng

E-Mail Website
Guest Editor
IBM Research-China, Beijing, China
Interests: anomaly detection; cloud applications; cloud platform; performance metrics; precision and recall; anomalous behavior; behavioral model; cloud computing; control flow graph; detection of abnormalities; diagnosis
Dr. Iraklis Varlamis

E-Mail Website
Guest Editor
Department of Informatics and Telematics, Harokopio University of Athens, Omirou 9, 17778 Athens, Greece
Interests: text mining; graph mining; social networks; healthcare; education
Special Issues, Collections and Topics in MDPI journals
Dr. Paul Townend

E-Mail Website
Guest Editor
Department of Computing Science, Umeå University, 901 87 Umeå, Sweden
Interests: green cloud computing; cloud-edge modelling; data centers; simulation; provenance; dependability

Special Issue Information

Dear Colleagues,

Technology has the power to transform our world, and this is especially evident in the realm of urban development. The convergence of big data and artificial intelligence (AI) with sustainable smart cities represents a pivotal shift in how we envision and manage urban environments and development. This Special Issue examines the transformative impact of big data-driven AI solutions in creating smarter, more efficient, resilient, and sustainable cities.

The submissions are expected to focus on critical themes such as smart city infrastructure, exploring the integration of IoT devices, data management, and urban planning through data-driven insights. Big data analytics for urban development highlights the role of predictive analytics in optimizing city services, traffic management, and environmental monitoring. AI services in smart cities examines AI-powered citizen engagement, city operations automation, energy efficiency, and public safety enhancements. Citizen-centric smart city solutions emphasize inclusive and accessible AI-driven services, citizen engagement, privacy, and collaborative approaches among the government, the private sector, and citizens. Additionally, sustainable smart city solutions, in balancing both development and ESG achievements, integrate and optimize full-stack concepts, from management systems and operation platforms to infrastructures for secure, safe, reliable, resilient, and green smart cities.

This Special Issue invites discussions on these diverse topics, aiming to shed light on the intricate interplay between big data and AI in fostering sustainable urban futures.

Prof. Dr. Jerry Gao
Dr. Fanjing Meng
Dr. Iraklis Varlamis
Dr. Paul Townend
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Smart Cities is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart city infrastructure
  • big data analytics for urban development
  • AI services in smart cities
  • citizen-centric smart city solutions
  • sustainable smart city solutions

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

31 pages, 3691 KiB  
Article
Enhancing Smart Home Security: Blockchain-Enabled Federated Learning with Knowledge Distillation for Intrusion Detection
by Mohammed Shalan, Md Rakibul Hasan, Yan Bai and Juan Li
Smart Cities 2025, 8(1), 35; https://doi.org/10.3390/smartcities8010035 - 17 Feb 2025
Cited by 1 | Viewed by 1084
Abstract
The increasing adoption of smart home devices has raised significant concerns regarding privacy, security, and vulnerability to cyber threats. This study addresses these challenges by presenting a federated learning framework enhanced with blockchain technology to detect intrusions in smart home environments. The proposed [...] Read more.
The increasing adoption of smart home devices has raised significant concerns regarding privacy, security, and vulnerability to cyber threats. This study addresses these challenges by presenting a federated learning framework enhanced with blockchain technology to detect intrusions in smart home environments. The proposed approach combines knowledge distillation and transfer learning to support heterogeneous IoT devices with varying computational capacities, ensuring efficient local training without compromising privacy. Blockchain technology is integrated to provide decentralized, tamper-resistant access control through Role-Based Access Control (RBAC), allowing only authenticated devices to participate in the federated learning process. This combination ensures data confidentiality, system integrity, and trust among devices. This framework’s performance was evaluated using the N-BaIoT dataset, showcasing its ability to detect anomalies caused by botnets such as Mirai and BASHLITE across diverse IoT devices. Results demonstrate significant improvements in intrusion detection accuracy, particularly for resource-constrained devices, while maintaining privacy and adaptability in dynamic smart home environments. These findings highlight the potential of this blockchain-enhanced federated learning system to offer a scalable, robust, and privacy-preserving solution for securing smart homes against evolving threats. Full article
(This article belongs to the Special Issue Big Data and AI Services for Sustainable Smart Cities)
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15 pages, 3263 KiB  
Article
Smart Green Energy Management for Campus: An Integrated Machine Learning and Reinforcement Learning Model
by Charan Teja Madabathula, Kunal Agrawal, Vijen Mehta, Swathi Kasarabada, Sai Srimai Kommamuri, Guannan Liu and Jerry Gao
Smart Cities 2025, 8(1), 30; https://doi.org/10.3390/smartcities8010030 - 13 Feb 2025
Viewed by 1030
Abstract
The increasing demand for energy efficiency and the integration of renewable energy sources have become crucial for sustainability in modern campuses. This work presents a smart green energy management system (SGEMS) that integrates a machine learning model and reinforcement learning (RL) to optimize [...] Read more.
The increasing demand for energy efficiency and the integration of renewable energy sources have become crucial for sustainability in modern campuses. This work presents a smart green energy management system (SGEMS) that integrates a machine learning model and reinforcement learning (RL) to optimize energy consumption and solar generation across a green campus. Using historical data from three campus buildings, we developed a predictive model to forecast short-term energy consumption and solar generation. The XGBoost algorithm, combined with RL, demonstrated superior performance in predicting energy consumption and generation, outperforming other models with a root mean square error (RMSE) of 14.72, a mean absolute error (MAE) of 12.00, and a mean absolute percentage error (MAPE) of 2.18%. This work proposes a web-based interface for real-time energy monitoring and decision-making, helping users forecast power shortages and manage energy usage effectively. The proposed approach provides a scalable solution for campuses aiming to reduce reliance on external grids and increase energy efficiency, setting a benchmark for future green campus initiatives. Full article
(This article belongs to the Special Issue Big Data and AI Services for Sustainable Smart Cities)
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35 pages, 6240 KiB  
Article
LLM Agents for Smart City Management: Enhancing Decision Support Through Multi-Agent AI Systems
by Anna Kalyuzhnaya, Sergey Mityagin, Elizaveta Lutsenko, Andrey Getmanov, Yaroslav Aksenkin, Kamil Fatkhiev, Kirill Fedorin, Nikolay O. Nikitin, Natalia Chichkova, Vladimir Vorona and Alexander Boukhanovsky
Smart Cities 2025, 8(1), 19; https://doi.org/10.3390/smartcities8010019 - 24 Jan 2025
Cited by 1 | Viewed by 2689
Abstract
This study investigates the implementation of LLM agents in smart city management, leveraging both the inherent language processing abilities of LLMs and the distributed problem solving capabilities of multi-agent systems for the improvement of urban decision making processes. A multi-agent system architecture combines [...] Read more.
This study investigates the implementation of LLM agents in smart city management, leveraging both the inherent language processing abilities of LLMs and the distributed problem solving capabilities of multi-agent systems for the improvement of urban decision making processes. A multi-agent system architecture combines LLMs with existing urban information systems to process complex queries and generate contextually relevant responses for urban planning and management. The research is focused on three main hypotheses testing: (1) LLM agents’ capability for effective routing and processing diverse urban queries, (2) the effectiveness of Retrieval-Augmented Generation (RAG) technology in improving response accuracy when working with local knowledge and regulations, and (3) the impact of integrating LLM agents with existing urban information systems. Our experimental results, based on a comprehensive validation dataset of 150 question–answer pairs, demonstrate significant improvements in decision support capabilities. The multi-agent system achieved pipeline selection accuracy of 94–99% across different models, while the integration of RAG technology improved response accuracy by 17% for strategic development queries and 55% for service accessibility questions. The combined use of document databases and service APIs resulted in the highest performance metrics (G-Eval scores of 0.68–0.74) compared to standalone LLM responses (0.30–0.38). Using St. Petersburg’s Digital Urban Platform as a testbed, we demonstrate the practical applicability of this approach to create integrated city management systems with support complex urban decision making processes. This research contributes to the growing field of AI-enhanced urban management by providing empirical evidence of LLM agents’ effectiveness in processing heterogeneous urban data and supporting strategic planning decisions. Our findings suggest that LLM-based multi-agent systems can significantly enhance the efficiency and accuracy of urban decision making while maintaining high relevance in responses. Full article
(This article belongs to the Special Issue Big Data and AI Services for Sustainable Smart Cities)
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