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Smart and Sustainable Cities and Rural Regions: Data-Driven Approaches for Resilient and Sustainable Infrastructure

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 434

Special Issue Editor


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Guest Editor
College of Engineering, University of Georgia, Athens, GA 30602, USA
Interests: sustainable and resilient infrastructure systems; smart mobility systems; big data mining and analytics; deep learning methods and applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue explores the role of advanced technologies and data-driven solutions in developing resilient, sustainable, and interconnected infrastructure across both urban and rural settings. As cities and rural regions evolve, their infrastructure needs are increasingly interlinked, requiring integrated approaches that support economic growth, environmental sustainability, and social well-being. Rapid urbanization, climate variability, and technological advancements are reshaping urban infrastructure. Rural areas, which are critical for food production, resource management, and cultural preservation, are modernizing to enhance their connectivity, service delivery, and resilience. Bridging urban and rural infrastructure gaps requires innovative, adaptable, and region-specific solutions.

Urban centers are leveraging smart technologies such as the Internet of Things (IoT), big data, and artificial intelligence to optimize infrastructure systems, including transportation networks, energy grids, and water management. These advancements enhance efficiency, reduce environmental impacts, and improve urban living conditions. Meanwhile, rural regions are embracing digital connectivity, precision agriculture, and decentralized energy solutions to enhance the reliability of their infrastructure and the accessibility of services. Challenges such as aging infrastructures, climate resilience, and equitable access to resources require coordinated strategies that align technological advancements with local needs.

This Special Issue will showcase case studies, methodologies, and policies that highlight how integrated, technology-driven infrastructure solutions can support both urban and rural regions. By fostering collaboration and leveraging smart systems, we can create more resilient, adaptive, and sustainable infrastructure that meets the needs of diverse communities and ensures long-term prosperity.

Dr. Jidong J. Yang
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

  • resilient infrastructure
  • data-driven solutions
  • digital transformation
  • Internet of Things (IoT)
  • sustainable development
  • big data analytics
  • rural connectivity
  • urban–rural integration

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Published Papers (1 paper)

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Research

19 pages, 4400 KiB  
Article
Smart Street Lighting Powered by Renewable Energy: A Multi-Criteria, Data-Driven Decision Framework
by Jiachen Bian and Jidong J. Yang
Sustainability 2025, 17(13), 5874; https://doi.org/10.3390/su17135874 - 26 Jun 2025
Viewed by 234
Abstract
Renewable energy sources, such as solar and wind power, are gaining increasing global attention. To facilitate their integration into transportation infrastructure, this paper proposes a multi-criteria assessment framework for identifying the most suitable renewable energy sources for street lighting at any given location. [...] Read more.
Renewable energy sources, such as solar and wind power, are gaining increasing global attention. To facilitate their integration into transportation infrastructure, this paper proposes a multi-criteria assessment framework for identifying the most suitable renewable energy sources for street lighting at any given location. The framework evaluates three key metrics: cost–benefit, reliability, and power generation potential, using time-series weather data. To demonstrate its effectiveness, we apply the framework to data from Georgia, USA. The results show that the proposed approach effectively classifies locations into four categories: solar-recommended, wind-recommended, hybrid-recommended, and no recommendation. Specifically, wind energy is primarily recommended in the southeastern region near the coastline, while solar energy is favored in the northwestern region. A hybrid of both sources is mainly recommended along the coast and in transitional areas. In several isolated parts of the northwest, neither energy source is recommended due to unfavorable weather conditions influenced by the local terrain. Since processing long-term time-series data is computationally intensive and challenging during inference, we train machine learning models, including Multilayer Perceptron (MLP) and Extreme Gradient Boosting (XGBoost), using temporally aggregated features for efficient and rapid decision-making. The MLP model achieves an overall accuracy of 92.4%, while XGBoost further improves accuracy to 94.3%. This study provides a practical reference for regional energy infrastructure planning, promoting optimized renewable energy use in street lighting through a robust, data-driven evaluation framework. Full article
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