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Advanced Research on Smart Cities Based on Data Processing and Intelligent Computing

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 June 2025 | Viewed by 11216

Special Issue Editors

Dr. Yujie Tang

E-Mail Website
Guest Editor
Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada
Interests: Internet of vehicles; artificial intelligence; 6G networking
Prof. Dr. Shu Fu

E-Mail Website
Guest Editor
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
Interests: B5G/6G ultra-dense cellular network; UAV; low orbit satellite communication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the boom of technology and the economy, there is a continuously increasing demand for a better and more convenient life, with enhanced sustainability, efficiency, and resilience. Smart cities are emerging with various advanced technologies, such as infrastructure monitoring, intelligent traffic management, and greenhouse gas mitigation. Constructing a smart city is an important part of improving the quality of urban life, and the development of smart cities requires the systematic analysis of relevant data. Data processing and intelligent computing which integrate data mining, data analysis, computer vision, deep learning, computation resource allocation, task offloading, and task downloading are promising technologies for smart cities. This poses new challenges in investigating these technologies as well as their potential applications in operating and managing various aspects of smart cities. Therefore, there is a strong motivation to explore methods or solutions through innovative and connected technology and data to handle constrained, complex, large-scale, and multi-objective optimization problems for smart cities, which has attracted extensive attention from both academics and industries.

This Special Issue seeks to present new ideas and approaches in the field of smart cities. It targets a wide range of areas, including intelligent transportation systems, smart campuses, digital logistics, efficient management, etc., as well as related and recent advances in data processing and intelligent computing for different fields of smart cities, such as massive data collection and analysis, large-scale computational science, artificial intelligence, city planning, intelligent infrastructures, machine learning, deep learning, and reinforcement learning. Simulations, datasets relevant to smart cities, and the deployment of algorithms on existing city areas are also covered.

The topics of interest for this Special Issue include, but are not limited to, the following topics:

  • Advanced strategies for intelligent computing;
  • High-performance data analysis and processing;
  • Machine learning, deep learning, and reinforcement learning;
  • Big-data-based applications, algorithms, and systems design;
  • Use cases and applications for intelligent transport and logistics systems;
  • Intelligent computing technologies for 5G/6G networking;
  • Wireless technologies for massive data communications;
  • Data security and privacy-aware communication and computing;
  • Intelligent tasks allocation and scheduling for smart cities;
  • Novel testing and validation methods for smart cities;
  • Systematic surveys or reviews for the development of smart cities.

Dr. Yujie Tang
Dr. Shu Fu
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 city
  • data processing
  • intelligent computing
  • machine learning, deep learning, reinforcement learning
  • experiment and validation

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Published Papers (5 papers)

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Research

37 pages, 3782 KiB  
Article
Smart Cities and Resident Well-Being: Using the BTOPSIS Method to Assess Citizen Life Satisfaction in European Cities
by Ewa Roszkowska and Tomasz Wachowicz
Appl. Sci. 2024, 14(23), 11051; https://doi.org/10.3390/app142311051 - 27 Nov 2024
Cited by 1 | Viewed by 774
Abstract
With rapid urbanization, maintaining a high quality of life (QoL) for city residents has become a critical challenge for policy-makers and urban planners. Smart cities, leveraging advanced technologies and data analytics, present a promising pathway to enhance urban services and promote sustainability. This [...] Read more.
With rapid urbanization, maintaining a high quality of life (QoL) for city residents has become a critical challenge for policy-makers and urban planners. Smart cities, leveraging advanced technologies and data analytics, present a promising pathway to enhance urban services and promote sustainability. This paper introduces an innovative adaptation of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, integrating a Belief Structure (BTOPSIS) to improve the evaluation and interpretation of survey data. Our approach effectively addresses the distribution of responses across categories and the uncertainty often present in such data, including missing or ambiguous answers. Additionally, we perform a sensitivity analysis to assess the stability of the BTOPSIS rankings under varying utility function parameters, further validating the robustness of our method. We apply this framework to the 2023 ‘Quality of Life in European Cities’ survey, analyzing diverse urban factors such as public transport, healthcare, cultural facilities, green spaces, education, air quality, noise levels, and cleanliness. Additionally, our study offers a comparative analysis of BTOPSIS against other multi-criteria methods used for evaluation data from this report, showcasing its strengths and limitations in addressing the dataset’s complexity. Our findings reveal significant variations in residents’ perceived QoL across European cities, both between cities and within countries. Zurich and Groningen rank highest in satisfaction, while Tirana, Skopje, and Palermo are ranked lowest. Notably, residents of cities with populations under 500,000 report higher satisfaction levels than those in larger cities, and satisfaction levels are generally higher in EU and EFTA cities compared to those in the Western Balkans, with the highest satisfaction observed in northern and western Member States. To aid urban planners and policy-makers, we propose a ranking tool using the BTOPSIS method, capturing nuanced resident perceptions of living conditions across cities. These insights provide valuable guidance for strategic urban development and advancing the smart city agenda across Europe. Full article
(This article belongs to the Special Issue Advanced Research on Smart Cities Based on Data Processing and Intelligent Computing)
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22 pages, 2265 KiB  
Article
Optimizing Smart City Strategies: A Data-Driven Analysis Using Random Forest and Regression Analysis
by Omer Bafail
Appl. Sci. 2024, 14(23), 11022; https://doi.org/10.3390/app142311022 - 27 Nov 2024
Viewed by 1100
Abstract
This study investigates the critical factors influencing smart city program success through a comprehensive data-driven analysis of 140 urban centers. Advanced machine learning techniques, specifically random forest algorithms, in conjunction with regression analysis, were employed to examine the correlations between 45 distinct attributes [...] Read more.
This study investigates the critical factors influencing smart city program success through a comprehensive data-driven analysis of 140 urban centers. Advanced machine learning techniques, specifically random forest algorithms, in conjunction with regression analysis, were employed to examine the correlations between 45 distinct attributes and respective smart city rankings. The findings reveal that the human development index (HDI) is a key predictor of smart city performance. Furthermore, the regression analysis revealed that elements such as education, healthcare, infrastructure, and digital services significantly enhance achieving higher HDI scores. Similarly, factors like education, sanitation, healthcare, and government transparency are closely associated with successfully implementing sharing platforms. These findings highlight the importance of investing in human capital, developing digital infrastructure, and promoting community engagement to create sustainable and resilient smart cities. Policymakers can utilize these findings to prioritize investments and devise effective strategies to improve their city’s ranking. Full article
(This article belongs to the Special Issue Advanced Research on Smart Cities Based on Data Processing and Intelligent Computing)
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24 pages, 1182 KiB  
Article
A Generative Artificial Intelligence Using Multilingual Large Language Models for ChatGPT Applications
by Nguyen Trung Tuan, Philip Moore, Dat Ha Vu Thanh and Hai Van Pham
Appl. Sci. 2024, 14(7), 3036; https://doi.org/10.3390/app14073036 - 4 Apr 2024
Cited by 9 | Viewed by 4995
Abstract
ChatGPT plays significant roles in the third decade of the 21st Century. Smart cities applications can be integrated with ChatGPT in various fields. This research proposes an approach for developing large language models using generative artificial intelligence models suitable for small- and medium-sized [...] Read more.
ChatGPT plays significant roles in the third decade of the 21st Century. Smart cities applications can be integrated with ChatGPT in various fields. This research proposes an approach for developing large language models using generative artificial intelligence models suitable for small- and medium-sized enterprises with limited hardware resources. There are many generative AI systems in operation and in development. However, the technological, human, and financial resources required to develop generative AI systems are impractical for small- and medium-sized enterprises. In this study, we present a proposed approach to reduce training time and computational cost that is designed to automate question–response interactions for specific domains in smart cities. The proposed model utilises the BLOOM approach as its backbone for using generative AI to maximum the effectiveness of small- and medium-sized enterprises. We have conducted a set of experiments on several datasets associated with specific domains to validate the effectiveness of the proposed model. Experiments using datasets for the English and Vietnamese languages have been combined with model training using low-rank adaptation to reduce training time and computational cost. In comparative experimental testing, the proposed model outperformed the ‘Phoenix’ multilingual chatbot model by achieving a 92% performance compared to ‘ChatGPT’ for the English benchmark. Full article
(This article belongs to the Special Issue Advanced Research on Smart Cities Based on Data Processing and Intelligent Computing)
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19 pages, 4132 KiB  
Article
Research on the Spatial and Temporal Distribution Evolution and Sustainable Development Mechanism of Smart Health and Elderly Care Demonstration Bases Based on GIS
by Xiaolong Chen, Bowen Chen, Hongfeng Zhang and Cora Un In Wong
Appl. Sci. 2024, 14(2), 780; https://doi.org/10.3390/app14020780 - 17 Jan 2024
Cited by 1 | Viewed by 1688
Abstract
Utilizing GIS technology and spatial analysis methodologies, this study endeavours to delve into and grasp the localized attributes of the regional units under investigation from a geographical vantage point, as well as the interrelationships among these units. This endeavour encompasses the identification and [...] Read more.
Utilizing GIS technology and spatial analysis methodologies, this study endeavours to delve into and grasp the localized attributes of the regional units under investigation from a geographical vantage point, as well as the interrelationships among these units. This endeavour encompasses the identification and quantification of developmental patterns, the assessment of trends, and the resolution of any intricate issues about geographical location to make prognostications and informed decisions. Classic spatial analysis techniques such as the geographic concentration index, kernel density analysis, Thiessen polygons, and spatial autocorrelation analysis (Moran’s I index) are employed in this inquiry. Initially, the study utilized the nearest neighbour index and geographic concentration index to gauge the equilibrium, proximity, and concentration of the spatiotemporal distribution of smart health elderly care demonstration bases across 31 provinces in China. Upon confirming the spatial clustering and imbalance of the distribution of elderly care demonstration bases in China, kernel density analysis was applied to compute the density of point features surrounding each output raster cell and to visually represent the spatiotemporal distribution status of the bases. Finally, Thiessen polygons and spatial autocorrelation analysis (Moran’s I index) were introduced to further elucidate and validate the spatial distribution patterns of the elderly care demonstration bases. The findings of the research reveal that smart health and elderly care bases in China manifest spatial clustering, predominantly concentrated in the central and eastern regions of the country. The overarching pattern embodies a spatial model characterized by a “concentration in three poles with multiple cores surrounding”. Ultimately, the study offers recommendations for the nexus between three principal mechanisms: market-driven development mechanisms, policy-driven development mechanisms, and technology-driven development mechanisms, advocating for the further progression of intelligent construction to attain the sustainable development of demonstration bases. This research furnishes a scientific foundation for the planning and industrial advancement of pertinent departments. Full article
(This article belongs to the Special Issue Advanced Research on Smart Cities Based on Data Processing and Intelligent Computing)
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15 pages, 1187 KiB  
Article
PPO-Based Joint Optimization for UAV-Assisted Edge Computing Networks
by Zhihui Liu, Qiwei Zhang and Yi Su
Appl. Sci. 2023, 13(23), 12828; https://doi.org/10.3390/app132312828 - 29 Nov 2023
Cited by 2 | Viewed by 1607
Abstract
In next-generation mobile communication scenarios, more and more user terminals (UEs) and edge computing servers (ECSs) are connected to the network. To ensure the experience of edge computing services, we designed an unmanned aerial vehicle (UAV)-assisted edge computing network application scenario. In the [...] Read more.
In next-generation mobile communication scenarios, more and more user terminals (UEs) and edge computing servers (ECSs) are connected to the network. To ensure the experience of edge computing services, we designed an unmanned aerial vehicle (UAV)-assisted edge computing network application scenario. In the considered scenario, the UAV acts as a relay node to forward edge computing tasks when the performance of the wireless channel between UEs and ECSs degrades. In order to minimize the average delay of edge computing tasks, we design the optimization problem of joint UE–ECS matching and UAV three-dimensional hovering position deployment. Further, we transform this mixed integer nonlinear programming into a continuous-variable decision process and design the corresponding Proximal Policy Optimization (PPO)-based joint optimization algorithm. Sufficient data pertaining to latency demonstrate that the suggested algorithm can obtain a seamless reward value when the number of training steps hits three million. This verifies the algorithm’s desirable convergence property. Furthermore, the algorithm’s efficacy has been confirmed through simulation in various environments. The experimental findings ascertain that the PPO-based co-optimization algorithm consistently attains a lower average latency rate and a minimum of 8% reduction in comparison to the baseline scenarios. Full article
(This article belongs to the Special Issue Advanced Research on Smart Cities Based on Data Processing and Intelligent Computing)
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