sensors-logo

Journal Browser

Journal Browser

Artificial Intelligence and Sensors Technology in Smart Cities

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2918

Special Issue Editor


E-Mail Website
Guest Editor
School of Information Technology, Deakin University, Geelong, VIC 3220, Australia
Interests: machine learning; federated learning; blockchain; remote sensing image analysis

Special Issue Information

Dear Colleagues,

We are pleased to announce a call for papers for a Special Issue of our esteemed journal that will focus on the intersection of artificial intelligence (AI) and sensors technology in the context of smart cities. As the world rapidly urbanizes, cities face significant challenges in terms of managing resources, enhancing efficiency, and improving the quality of life for their residents. This Special Issue aims to explore the potential of AI and sensor technologies in addressing these challenges and creating sustainable, intelligent urban environments.

This Special Issue aims to bring together cutting-edge research and innovative solutions that demonstrate the application of AI and sensors technology in various aspects of smart cities. Contributions are invited from researchers, practitioners, and industry experts to showcase their work, share insights, and foster a deeper understanding of the potential of these technologies. The key objectives of this Special Issue include:

  • Exploring the integration of AI algorithms and techniques with sensor networks in the context of smart cities.
  • Investigating the role of AI in data analysis, processing, and interpretation from sensor networks.
  • Examining the use of AI and sensor technologies for real-time monitoring, prediction, and decision-making in smart city systems.
  • Addressing the challenges and opportunities in implementing AI and sensor-based solutions for urban infrastructure, transportation, energy management, public safety, and environmental monitoring.
  • Evaluating the impact of AI and sensor technologies on citizen engagement, inclusivity, and overall quality of life in smart cities.
  • Discussing ethical considerations, privacy concerns, and legal frameworks related to the use of AI and sensor technologies in urban environments.

We invite submissions of original research papers, case studies, review articles, and survey papers on various topics related to AI and sensors technology in smart cities, including but not limited to:

  • AI-driven sensor networks for urban monitoring and control.
  • Machine learning algorithms for sensor data analysis in smart cities.
  • Edge machine learning, e.g., federated learning for smart cities applications
  • Intelligent transportation systems and traffic management using sensors and AI.
  • Energy-efficient buildings and smart grids enabled by AI and sensors.
  • Sensor-based environmental monitoring and pollution control in urban areas.
  • Public safety and emergency response systems employing AI and sensor technologies.
  • Citizen engagement platforms and participatory sensing in smart cities.
  • Privacy-preserving approaches and data governance in AI and sensor deployments.
  • Socio-economic impacts of AI and sensors in shaping future cities.
  • Smart governance and policymaking with AI and sensor-based solutions.

Dr. Md Palash Uddin
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. 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

  • artificial intelligence
  • sensors technology
  • smart cities
  • intelligent transportation
  • edge intelligence
  • intelligent sensors

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

17 pages, 2238 KiB  
Article
A Vehicle-Edge-Cloud Framework for Computational Analysis of a Fine-Tuned Deep Learning Model
by M. Jalal Khan, Manzoor Ahmed Khan, Sherzod Turaev, Sumbal Malik, Hesham El-Sayed and Farman Ullah
Sensors 2024, 24(7), 2080; https://doi.org/10.3390/s24072080 - 25 Mar 2024
Viewed by 783
Abstract
The cooperative, connected, and automated mobility (CCAM) infrastructure plays a key role in understanding and enhancing the environmental perception of autonomous vehicles (AVs) driving in complex urban settings. However, the deployment of CCAM infrastructure necessitates the efficient selection of the computational processing layer [...] Read more.
The cooperative, connected, and automated mobility (CCAM) infrastructure plays a key role in understanding and enhancing the environmental perception of autonomous vehicles (AVs) driving in complex urban settings. However, the deployment of CCAM infrastructure necessitates the efficient selection of the computational processing layer and deployment of machine learning (ML) and deep learning (DL) models to achieve greater performance of AVs in complex urban environments. In this paper, we propose a computational framework and analyze the effectiveness of a custom-trained DL model (YOLOv8) when deployed in diverse devices and settings at the vehicle-edge-cloud-layered architecture. Our main focus is to understand the interplay and relationship between the DL model’s accuracy and execution time during deployment at the layered framework. Therefore, we investigate the trade-offs between accuracy and time by the deployment process of the YOLOv8 model over each layer of the computational framework. We consider the CCAM infrastructures, i.e., sensory devices, computation, and communication at each layer. The findings reveal that the performance metrics results (e.g., 0.842 [email protected]) of deployed DL models remain consistent regardless of the device type across any layer of the framework. However, we observe that inference times for object detection tasks tend to decrease when the DL model is subjected to different environmental conditions. For instance, the Jetson AGX (non-GPU) outperforms the Raspberry Pi (non-GPU) by reducing inference time by 72%, whereas the Jetson AGX Xavier (GPU) outperforms the Jetson AGX ARMv8 (non-GPU) by reducing inference time by 90%. A complete average time comparison analysis for the transfer time, preprocess time, and total time of devices Apple M2 Max, Intel Xeon, Tesla T4, NVIDIA A100, Tesla V100, etc., is provided in the paper. Our findings direct the researchers and practitioners to select the most appropriate device type and environment for the deployment of DL models required for production. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors Technology in Smart Cities)
Show Figures

Figure 1

Review

Jump to: Research

22 pages, 5459 KiB  
Review
Trends in Digital Twin Framework Architectures for Smart Cities: A Case Study in Smart Mobility
by Evanthia Faliagka, Eleni Christopoulou, Dimitrios Ringas, Tanya Politi, Nikos Kostis, Dimitris Leonardos, Christos Tranoris, Christos P. Antonopoulos, Spyros Denazis and Nikolaos Voros
Sensors 2024, 24(5), 1665; https://doi.org/10.3390/s24051665 - 4 Mar 2024
Cited by 2 | Viewed by 1746
Abstract
The main aim of this paper is to present an innovative approach to addressing the challenges of smart mobility exploiting digital twins within the METACITIES initiative. We have worked on this issue due to the increasing complexity of urban transportation systems, coupled with [...] Read more.
The main aim of this paper is to present an innovative approach to addressing the challenges of smart mobility exploiting digital twins within the METACITIES initiative. We have worked on this issue due to the increasing complexity of urban transportation systems, coupled with the urgent need to improve efficiency, safety, and sustainability in cities. The work presented in this paper is part of the project METACITIES, an Excellence Hub that spans a large geographical area, that of Southeastern Europe. The approach of the Greek innovation ecosystem of METACITIES involves leveraging digital twin technology to create intelligent replicas of urban mobility environments, enabling real-time monitoring, analysis, and decision making. Through use cases such as “Smart Parking”, “Environmental Behavior Analysis on Traffic Incidents”, and “Emergency Management”, we demonstrate how digital twins can optimize traffic flow, mitigate environmental impact, and enhance emergency response; these use cases will be tested on a small scale, before deciding on implementation at a larger and more expensive scale. The final outcome is the METACITIES Architecture for smart mobility, which will be part of an Open Digital Twin Framework capable of evolving a smart city into a metacity. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors Technology in Smart Cities)
Show Figures

Figure 1

Back to TopTop