Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
Sustainable Smart Cities: Leveraging AI-Driven IoT Systems and Sensors
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sustainable Smart Cities: Leveraging AI-Driven IoT Systems and Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 361

Special Issue Editors

Dr. Pablo Chamoso

E-Mail Website
Guest Editor
BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain
Interests: artificial intelligence; smart cities; machine learning; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Guillermo Hernández

E-Mail Website
Guest Editor
BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain
Interests: artificial intelligence; smart cities; machine learning; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of sustainable smart cities is essential to address the environmental, social, and economic challenges brought by rapid urbanization. This Special Issue, “Sustainable Smart Cities: Leveraging AI-Driven IoT Systems and Sensors”, focuses on the integration of cutting-edge sensor systems, artificial intelligence (AI), and Internet of Things (IoT) technologies to create efficient, resilient, and inclusive urban environments. Its scope aligns perfectly with Sensors by emphasizing the innovative applications of sensor networks for urban infrastructure optimization, environmental monitoring, energy management, and public health improvement. Researchers are invited to showcase how advanced sensors and AI-driven IoT solutions enable real-time data collection, analysis, and action, fostering smarter decision-making processes.

The Special Issue also welcomes studies addressing demographic challenges, such as population aging and urban-rural dynamics, within the smart city paradigm. By covering topics, such as smart grids, intelligent transportation systems, waste management, and citizen-centric solutions, it offers a platform to explore how sensors serve as the backbone of sustainable cities. Researchers, practitioners, and policymakers are encouraged to share practical frameworks, novel technologies, and case studies to inspire the next generation of urban innovation.

Dr. Pablo Chamoso
Dr. Guillermo Hernández
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. Sensors 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 2600 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
  • AI-driven IoT
  • sensor networks
  • urban infrastructure
  • environmental monitoring
  • smart grids
  • intelligent transportation
  • public health systems
  • demographic challenges
  • urban innovation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Other

24 pages, 7395 KB  
Systematic Review
Advancements in Artificial Intelligence and Machine Learning for Occupational Risk Prevention: A Systematic Review on Predictive Risk Modeling and Prevention Strategies
by Pablo Armenteros-Cosme, Marcos Arias-González, Sergio Alonso-Rollán, Sergio Márquez-Sánchez and Albano Carrera
Sensors 2025, 25(17), 5419; https://doi.org/10.3390/s25175419 - 2 Sep 2025
Abstract
Background: Occupational risk prevention is a critical discipline for ensuring safe working conditions and minimizing accidents and occupational diseases. With the rise of artificial intelligence (AI) and machine learning (ML), these approaches are increasingly utilized for predicting and preventing workplace hazards. This systematic [...] Read more.
Background: Occupational risk prevention is a critical discipline for ensuring safe working conditions and minimizing accidents and occupational diseases. With the rise of artificial intelligence (AI) and machine learning (ML), these approaches are increasingly utilized for predicting and preventing workplace hazards. This systematic review aims to identify, evaluate, and synthesize existing literature on the use of AI algorithms for detecting and predicting hazardous environments and occupational risks in the workplace, focusing on predictive modeling and prevention strategies. Methods: A systematic literature review was conducted following the PRISMA 2020 protocol, with minor adaptations to include conference proceedings and technical reports due to the topic’s emerging and multidisciplinary nature. Searches were performed in IEEE Digital Library, PubMed, Scopus, and Web of Science, with the last search conducted on 1 August 2024. Only peer-reviewed articles published from 2019 onwards and written in English were included. Systematic literature reviews were explicitly excluded. The screening process involved duplicate removal (reducing 209 initial documents to 183 unique ones), a preliminary screening based on titles, abstracts, and keywords (further reducing to 92 articles), and a detailed full-text review. During the full-text review, study quality was assessed using six quality assessment (QA) questions, where articles receiving a total score below 4.5 or 0 in any QA question were excluded. This rigorous process resulted in the selection of 61 relevant articles for quantitative and qualitative analysis. Results: The analysis revealed a growing interest in the field, with a clear upward trend in publications from 2021 to 2023, and a continuation of growth into 2024. The most significant contributions originated from countries such as China, South Korea, and India. Applications primarily focused on high-risk sectors, notably construction, mining, and manufacturing. The most common approach involved the use of visual data captured by cameras, which constituted over 40% of the reviewed studies, processed using deep learning (DL) models, particularly Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO). Conclusions: The study highlights current limitations, including an over-reliance on visual data (especially challenging in low-visibility environments) and a lack of methodological standardization for AI-based risk detection systems. Future research should emphasize the integration of multimodal data (visual, environmental, physiological) and the development of interpretable AI models (XAI) to enhance accuracy, transparency, and trust in hazard detection systems. Addressing long-term societal implications, such as privacy and potential worker displacement, necessitates transparent data policies and robust regulatory frameworks. Full article
(This article belongs to the Special Issue Sustainable Smart Cities: Leveraging AI-Driven IoT Systems and Sensors)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop