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Novel Trends in Sensor Technology Applications for Intelligent Urban Infrastructures
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Novel Trends in Sensor Technology Applications for Intelligent Urban Infrastructures

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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 1738

Special Issue Editors

Dr. Mauro Fadda

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy
Interests: telecommunications; multimedia; wireless sensor networks; 5G and 6G mobile communications
Special Issues, Collections and Topics in MDPI journals
Dr. Pietro Ruiu

E-Mail Website
Guest Editor
Department of Biomedical Science, University of Sassari, 07100 Sassari, Italy
Interests: cloud computing; computing infrastructures; machine learning; computer vision; biometrics

Special Issue Information

Dear Colleagues,

New communication technologies, such as 5G and 6G, promise high-bandwidth and real-time capabilities that enable many new use cases in urban environments, such as autonomous driving, vehicle-to-everything (V2X) communications, the Internet of Things (IoT), virtual and augmented reality, or multi-gigabit wireless mobile broadband and fixed broadband wireless access. Communication standards continue to evolve to reach lower latencies, faster data rates, improved coverage, and higher reliability, granting services to a growing number of connections.

The urban environment is a typical use case where heterogeneous devices, each capable of carrying out one or more functions, can cooperate or act individually to improve the way that citizens experience the urban environment or simplify management operations by competent authorities.

The aim of this Special Issue is to bring together innovative developments and applications for intelligent urban infrastructures. Papers addressing new insights in the development, application, and performance benefits of using sensors and other intelligent devices in smart urban environments, improving current methods and technologies, are welcome. Articles may include, but are not limited to, the following topics:

  • New development of 5G- and 6G-based intelligent urban infrastructures.
  • Integration of sensors and IoT systems in smart urban environments.
  • Emerging 5G and 6G standards for new sensor systems and applications.
  • Cloud and distributed computing solutions for intelligent urban infrastructures.
  • Use cases of 5G- and 6G-sensor-based smart urban solutions.
  • Machine learning methods for sensor-based systems and applications in urban environments.
  • Architecture proposals for sensor-based urban intelligent systems.
  • Virtual and augmented reality use cases and solutions for future cities.
  • Computer vision applications for urban sensor-based scenarios.
  • Privacy issues and solutions in sensor-based systems and applications.

Dr. Mauro Fadda
Dr. Pietro Ruiu
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

  • intelligent city
  • smart city
  • 5G-based solutions
  • 6G-based solutions
  • intelligent infrastructure
  • smart infrastructure
  • intelligent sensors
  • cloud computing

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Published Papers (2 papers)

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20 pages, 6220 KiB  
Article
Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery
by Ruikang Luo, Yaofeng Song, Longfei Ye and Rong Su
Sensors 2024, 24(23), 7662; https://doi.org/10.3390/s24237662 - 29 Nov 2024
Viewed by 321
Abstract
Accurate vehicle type classification plays a significant role in intelligent transportation systems. It is critical to understand the road conditions and usually contributive for the traffic light control system to respond correspondingly to alleviate traffic congestion. New technologies and comprehensive data sources, such [...] Read more.
Accurate vehicle type classification plays a significant role in intelligent transportation systems. It is critical to understand the road conditions and usually contributive for the traffic light control system to respond correspondingly to alleviate traffic congestion. New technologies and comprehensive data sources, such as aerial photos and remote sensing data, provide richer and higher-dimensional information. In addition, due to the rapid development of deep neural network technology, image-based vehicle classification methods can better extract underlying objective features when processing data. Recently, several deep learning models have been proposed to solve this problem. However, traditional purely convolution-based approaches have constraints on global information extraction, and complex environments such as bad weather seriously limit their recognition capability. To improve vehicle type classification capability under complex environments, this study proposes a novel Densely Connected Convolutional Transformer-in-Transformer Neural Network (Dense-TNT) framework for vehicle type classification by stacking Densely Connected Convolutional Network (DenseNet) and Transformer-in-Transformer (TNT) layers. Vehicle data for three regions under four different weather conditions were deployed to evaluate the recognition capability. Our experimental findings validate the recognition ability of the proposed vehicle classification model, showing little decay even under heavy fog. Full article
(This article belongs to the Special Issue Novel Trends in Sensor Technology Applications for Intelligent Urban Infrastructures)
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23 pages, 23514 KiB  
Article
Deep-Learning-Based Automated Building Construction Progress Monitoring for Prefabricated Prefinished Volumetric Construction
by Wei Png Chua and Chien Chern Cheah
Sensors 2024, 24(21), 7074; https://doi.org/10.3390/s24217074 - 2 Nov 2024
Viewed by 1043
Abstract
Prefabricated prefinished volumetric construction (PPVC) is a relatively new technique that has recently gained popularity for its ability to improve flexibility in scheduling and resource management. Given the modular nature of PPVC assembly and the large amounts of visual data amassed throughout a [...] Read more.
Prefabricated prefinished volumetric construction (PPVC) is a relatively new technique that has recently gained popularity for its ability to improve flexibility in scheduling and resource management. Given the modular nature of PPVC assembly and the large amounts of visual data amassed throughout a construction project today, PPVC building construction progress monitoring can be conducted by quantifying assembled PPVC modules within images or videos. As manually processing high volumes of visual data can be extremely time consuming and tedious, building construction progress monitoring can be automated to be more efficient and reliable. However, the complex nature of construction sites and the presence of nearby infrastructure could occlude or distort visual data. Furthermore, imaging constraints can also result in incomplete visual data. Therefore, it is hard to apply existing purely data-driven object detectors to automate building progress monitoring at construction sites. In this paper, we propose a novel 2D window-based automated visual building construction progress monitoring (WAVBCPM) system to overcome these issues by mimicking human decision making during manual progress monitoring with a primary focus on PPVC building construction. WAVBCPM is segregated into three modules. A detection module first conducts detection of windows on the target building. This is achieved by detecting windows within the input image at two scales by using YOLOv5 as a backbone network for object detection before using a window detection filtering process to omit irrelevant detections from the surrounding areas. Next, a rectification module is developed to account for missing windows in the mid-section and near-ground regions of the constructed building that may be caused by occlusion and poor detection. Lastly, a progress estimation module checks the processed detections for missing or excess information before performing building construction progress estimation. The proposed method is tested on images from actual construction sites, and the experimental results demonstrate that WAVBCPM effectively addresses real-world challenges. By mimicking human inference, it overcomes imperfections in visual data, achieving higher accuracy in progress monitoring compared to purely data-driven object detectors. Full article
(This article belongs to the Special Issue Novel Trends in Sensor Technology Applications for Intelligent Urban Infrastructures)
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