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Sustainable Smart Cities: From Technologies and Optimization Problems to Future Trends and Solutions

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 1384

Special Issue Editor


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Guest Editor
Department of Mathematics and Computer Science at the Royal Military College of Canada (RMC), Kingston, ON K7K 7B4, Canada
Interests: machine learning; smart cities; big data analytics; Internet of Things; Industry 4.0; 5G; modeling and optimization of wireless communications systems; intelligent transportation systems
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Special Issue Information

Dear Colleagues:

Various engineering technologies are being integrated into the construction of "smart cities" across multiple countries. Several subfields of engineering, such as the Internet of Things, mobile networks, smart grids, sixth-generation (6G) networks, intelligent transportation systems (ITSs), edge computing, autonomous vehicles, electrical cars, and artificial intelligence, to name a few, are converging to explore feasible alternatives to the existing paradigm. These emerging technologies will have a significant impact on the enhancement of smart cities. This will manifest in various ways, such as improving public transportation, mitigating traffic congestion, optimizing municipal services in terms of cost, ensuring the safety and well-being of citizens, minimizing energy usage, and mitigating pollution. Furthermore, these technologies are being combined and utilized by smart cities to achieve the environmental objectives of the Sustainable Development Goals. This convergence is known as "sustainable smart cities" and is recognized for its synergistic potential. However, designing, optimizing, implementing, and deploying those systems entail various challenges. One of the primary obstacles is ensuring the efficient operation of these systems in dynamic environments characterized by diverse conditions. Furthermore, it is imperative for collaborative smart city applications to effectively tackle concerns about the security and privacy of shared data.

This Special Issue seeks high-quality papers that advance both theoretical knowledge and practical implementations of future technologies in the context of sustainable smart cities. This feature topic aims to facilitate collaboration between academic and industrial researchers to analyze and address the key opportunities and challenges associated with the application of emerging technologies. The goal is to generate innovative solutions and valuable insights that can contribute to the design of modern and sustainable cities.

Original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • The design of AI-based energy-efficient architecture in sustainable smart cities;
  • Theoretical analysis of energy management in sustainable smart cities;
  • Security and privacy in sustainable smart cities;
  • Energy optimization of federated learning in sustainable smart cities;
  • Smart mobility solutions for pollution control: the role of intelligent transportation systems;
  • Artificial-intelligence-based adaptive control systems for sustainable smart city transportation;
  • Green cloud computing and data center networks in smart cities;
  • Artificial intelligence trends in intelligent transportation systems for real-time connected and autonomous systems;
  • Artificial intelligence techniques for infrastructure monitoring and next-gen smart cities;
  • AI-based techniques in modern electric power systems for intelligent energy management;
  • Novel algorithms and emerging models for ITSs for creating situational awareness;
  • Future perspectives of situational awareness algorithms for smart transportation;
  • Performance measurement, evaluation, and monitoring tools for future networks in sustainable smart cities;
  • Zero-touch Service Provisioning for future networks in sustainable smart cities.

We look forward to receiving your contributions.

Prof. Dr. Abdellah Chehri
Guest Editor

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. Sustainability 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

  • sustainable smart cities
  • energy efficiency
  • intelligent transportation systems
  • green cloud computing
  • smart grid

Published Papers (1 paper)

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Research

23 pages, 3677 KiB  
Article
Using Generic Direct M-SVM Model Improved by Kohonen Map and Dempster–Shafer Theory to Enhance Power Transformers Diagnostic
by Mounia Hendel, Fethi Meghnefi, Mohamed El Amine Senoussaoui, Issouf Fofana and Mostefa Brahami
Sustainability 2023, 15(21), 15453; https://doi.org/10.3390/su152115453 - 30 Oct 2023
Cited by 4 | Viewed by 856
Abstract
Many power transformers throughout the world are nearing or have gone beyond their theoretical design life. Since these important assets represent approximately 60% of the cost of the substation, monitoring their condition is necessary. Condition monitoring helps in the decision to perform timely [...] Read more.
Many power transformers throughout the world are nearing or have gone beyond their theoretical design life. Since these important assets represent approximately 60% of the cost of the substation, monitoring their condition is necessary. Condition monitoring helps in the decision to perform timely maintenance, to replace equipment or extend its life after evaluating if it is degraded. The challenge is to prolong its residual life as much as possible. Dissolved Gas Analysis (DGA) is a well-established strategy to warn of fault onset and to monitor the transformer’s status. This paper proposes a new intelligent system based on DGA; the aim being, on the one hand, to overcome the conventional method weaknesses; and, on the other hand, to improve the transformer diagnosis efficiency by using a four-step powerful artificial intelligence method. (1) Six descriptor sets were built and then improved by the proposed feature reduction approach. Indeed, these six sets are combined and presented to a Kohonen map (KSOM), to cluster the similar descriptors. An averaging process was then applied to the grouped data, to reduce feature dimensionality and to preserve the complete information. (2) For the first time, four direct Multiclass Support Vector Machines (M-SVM) were introduced on the Generic Model basis; each one received the KSOM outputs. (3) Dempster–Shafer fusion was applied to the nine membership probabilities returned by the four M-SVM, to improve the accuracy and to support decision making. (4) An output post-processing approach was suggested to overcome the contradictory evidence problem. The achieved AUROC and sensitivity average percentages of 98.78–95.19% (p-value < 0.001), respectively, highlight the remarkable proposed system performance, bringing a new insight to DGA analysis. Full article
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