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Applied Sciences
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Intelligent Computing for Sustainable Smart Cities
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Intelligent Computing for Sustainable Smart Cities

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 1432

Special Issue Editors

Dr. Junho Jeong

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Dongguk University, Seoul 04620, Republic of Korea
Interests: computer security; privacy preserving; distributed system; network security; secure software
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jin-Woo Jung

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Dongguk University, Seoul 04620, Republic of Korea
Interests: intelligent robot; intelligent vehicles; intelligent control
Special Issues, Collections and Topics in MDPI journals
Dr. Seungmin Oh

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Kongju National University, Cheonan 31080, Republic of Korea
Interests: computer network; internet of everything
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue titled “Intelligent Computing for Smart Cities” covers a variety of research that applies the latest artificial intelligence and computing technologies to improve urban efficiency and quality of life. It explores innovative technologies and methodologies related to the design, development, management, maintenance, and security of smart cities. It is designed for researchers, policymakers, engineers, and urban planners who are involved in the development and implementation of smart city initiatives. It aims to provide valuable insights and foster collaboration to advance the field of intelligent computing in urban contexts. By addressing these themes, this Special Issue seeks to contribute to the development of smarter, more efficient, and sustainable cities, ultimately improving the quality of life for urban residents.

  1. Smart infrastructure:
  • Use of Internet of Things (IoT) devices and sensors for real-time data collection and analysis.
  • Intelligent management of urban infrastructure such as transportation systems, power grids, and water resources.
  1. Data analytics and predictive modeling:
  • Leveraging big data analytics to solve urban issues and support decision-making processes.
  • Utilizing machine learning and deep learning to predict urban phenomena and trends.
  1. Intelligent transportation systems:
  • Development of autonomous vehicles and vehicle-to-everything (V2X) communication technologies.
  • Algorithms for optimizing traffic flow and reducing congestion.
  1. Energy management:
  • Integration of smart grids and renewable energy sources.
  • Systems to enhance energy efficiency and manage consumption intelligently.
  1. Public safety and security:
  • Implementation of smart surveillance systems and emergency response management.
  • Cybersecurity and data privacy protection in smart city applications.
  1. Environmental monitoring and management:
  • Monitoring of air quality, noise levels, temperature, and other environmental parameters.
  • Solutions for environmental protection and sustainability.
  1. Smart healthcare:
  • Development of telemedicine and remote health monitoring systems.
  • Use of public health information, genetic data, and medical records for developing risk prediction models and providing healthcare services.

Dr. Junho Jeong
Prof. Dr. Jin-Woo Jung
Dr. Seungmin Oh
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. Applied Sciences 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 2400 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 infrastructure
  • data analytics and predictive modeling
  • intelligent transportation systems
  • energy management
  • public safety and security
  • environmental monitoring and management
  • smart healthcare

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

Published Papers (2 papers)

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Research

17 pages, 1455 KiB  
Article
Enhanced Graph Autoencoder for Graph Anomaly Detection Using Subgraph Information
by Chi Zhang and Jin-Woo Jung
Appl. Sci. 2025, 15(15), 8691; https://doi.org/10.3390/app15158691 - 6 Aug 2025
Viewed by 258
Abstract
Graph anomaly detection aims at identifying rare, unusual entities in attributed networks with respect to their patterns or structures that deviate significantly from the majority within a graph. Over the years, extensive efforts in this field have been dedicated to the powerful capability [...] Read more.
Graph anomaly detection aims at identifying rare, unusual entities in attributed networks with respect to their patterns or structures that deviate significantly from the majority within a graph. Over the years, extensive efforts in this field have been dedicated to the powerful capability of attributed networks to model real-world systems. Given the scarcity of labeled anomalies, current research primarily emphasizes model design via unsupervised learning. Graph autoencoders have been widely utilized for such purposes, leveraging the outstanding capabilities of Graph Neural Networks to model graph structured data. However, most existing graph autoencoder-based anomaly detectors do not exploit the nodes’ local subgraph information, limiting their ability to comprehensively understand the network for better representation learning. Moreover, these methods place greater emphasis on the attribute reconstruction process while neglecting the structure reconstruction aspect. This paper proposes an enhanced graph autoencoder framework for graph anomaly detection tasks that incorporates a subgraph extraction and aggregation preprocessing stage to utilize the nodes’ local topological information for enhanced embedding generation and to induce an additional node–subgraph view through model learning. A graph structure learning-based decoder is introduced as the structure decoder for better relationship learning. Finally, during the anomaly scoring stage, a node neighborhood selection technique is applied to enhance the detection performance. The effectiveness of the proposed framework is demonstrated through comprehensive experiments conducted on six commonly used real-world datasets. Full article
(This article belongs to the Special Issue Intelligent Computing for Sustainable Smart Cities)
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21 pages, 7622 KiB  
Article
Analyzing Transportation Network Vulnerability to Critical-Link Attacks Through Topology Changes and Traffic Volume Assessment
by Kalpana Ldchn, Teppei Kato and Kazushi Sano
Appl. Sci. 2025, 15(8), 4099; https://doi.org/10.3390/app15084099 - 8 Apr 2025
Viewed by 664
Abstract
As a critical infrastructure, the transportation network impacts health, safety, comfort, and the economy, making it highly vulnerable to disruptions that significantly affect social and economic well-being. To maintain optimal service during such disruptions, the critical links that are vulnerable to disruptions must [...] Read more.
As a critical infrastructure, the transportation network impacts health, safety, comfort, and the economy, making it highly vulnerable to disruptions that significantly affect social and economic well-being. To maintain optimal service during such disruptions, the critical links that are vulnerable to disruptions must be identified and their impact on network performance must be understood. This study proposes a method for identifying network vulnerabilities by targeting critical links based on topological parameters, assessing worst-case scenarios under severe conditions. These parameters serve as proxies for performance and are utilized to generate critical-link attacks to assess the network vulnerability. In addition, this study proposes a straightforward and simplistic modeling framework using topological parameters to assess the impact of such attacks on traffic flow changes. To characterize network performance and traffic volume changes under critical-link attacks, this study utilizes the complementary cumulative distribution function (CCDF), which highlights the upper tail of the distribution where extreme or rare events occur. The proposed method was applied to a real network in the Colombo Municipal Council (CMC) area in Sri Lanka. The findings of this study will help us understand the impact of critical-link attacks on transportation network performance and traffic flow and develop proactive policies to address vulnerabilities and improve overall network performance. Full article
(This article belongs to the Special Issue Intelligent Computing for Sustainable Smart Cities)
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