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Artificial Intelligence Methods for Smart Cities—2nd Edition
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Artificial Intelligence Methods for Smart Cities—2nd Edition

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

Deadline for manuscript submissions: 25 June 2024 | Viewed by 1154

Special Issue Editors

Prof. Dr. Salvatore Carta

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy
Interests: human-computer interaction; persuasive computing; recommender systems; machine learning; deep neural networks; time series
Special Issues, Collections and Topics in MDPI journals
Dr. Silvio Barra

E-Mail Website
Guest Editor
Department of Electrical and Information Technologies Engineering, University of Naples, “Federico II”, Corso Umberto I, 40, 80138 Naples, Italy
Interests: pattern recognition; biometrics; image processing; financial forecasting; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandro Sebastian Podda

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy
Interests: artificial intelligence; deep learning; information security; financial forecasting; blockchain; smart contracts
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart cities are steadily becoming more prominent, promising to improve the daily life of citizens and support their lifestyle habits by proposing subject-oriented services. In urban scenarios, this improvement is mainly directed towards people and vehicles, which may benefit from surveillance systems that provide services and guarantee safe living environments.

Novel artificial intelligence methods, techniques, and systems—particularly those based on machine or deep learning, computer vision, and the Internet of Things—are emerging to solve real-life problems ranging from video surveillance, road safety and traffic monitoring, and the prevention of accidents or critical events, to intelligent transportation and the management of public services. In fact, AI-based approaches, also leveraging IoT or cloud networks, may serve as underlying methods to tackle this wide range of problems.

This Special Issue aims to gather research proposing systems, approaches, solutions, and experimental results that contribute in an original and innovative way to all topics related to smart cities, in order to stimulate and increase scientific production in this rapidly growing area of research.

Prof. Dr. Salvatore Carta
Dr. Silvio Barra
Dr. Alessandro Sebastian Podda
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

  • artificial intelligence methods for smart cities
  • anomaly detection
  • people/objects detection
  • gait analysis in uncontrolled scenarios
  • background/foreground segmentations in urban scenarios
  • vehicle/drone tracking
  • video surveillance in smart cities
  • crowd analysis
  • traffic light management
  • smart cities databases
  • car reidentification
  • license plate recognition
  • object trajectory estimation/prediction
  • biometric recognition/verification in smart environments
  • IoT architectures and applications in smart cities and smart environments
  • intelligent transportation systems

Related Special Issue

Published Papers (2 papers)

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Research

19 pages, 1702 KiB  
Article
Proposal of a Machine Learning Approach for Traffic Flow Prediction
by Mariaelena Berlotti, Sarah Di Grande and Salvatore Cavalieri
Sensors 2024, 24(7), 2348; https://doi.org/10.3390/s24072348 - 07 Apr 2024
Viewed by 435
Abstract
Rapid global urbanization has led to a growing urban population, posing challenges in transportation management. Persistent issues such as traffic congestion, environmental pollution, and safety risks persist despite attempts to mitigate them, hindering urban progress. This paper focuses on the critical need for [...] Read more.
Rapid global urbanization has led to a growing urban population, posing challenges in transportation management. Persistent issues such as traffic congestion, environmental pollution, and safety risks persist despite attempts to mitigate them, hindering urban progress. This paper focuses on the critical need for accurate traffic flow forecasting, considered one of the main effective solutions for containing traffic congestion in urban scenarios. The challenge of predicting traffic flow is addressed by proposing a two-level machine learning approach. The first level uses an unsupervised clustering model to extract patterns from sensor-generated data, while the second level employs supervised machine learning models. Although the proposed approach requires the availability of data from traffic sensors to realize the training of the machine learning models, it allows traffic flow prediction in urban areas without sensors. In order to verify the prediction capability of the proposed approach, a real urban scenario is considered. Full article
(This article belongs to the Special Issue Artificial Intelligence Methods for Smart Cities—2nd Edition)
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19 pages, 7067 KiB  
Article
Deep Reinforcement Learning-Empowered Cost-Effective Federated Video Surveillance Management Framework
by Dilshod Bazarov Ravshan Ugli, Alaelddin F. Y. Mohammed, Taeheum Na and Joohyung Lee
Sensors 2024, 24(7), 2158; https://doi.org/10.3390/s24072158 - 27 Mar 2024
Viewed by 465
Abstract
Video surveillance systems are integral to bolstering safety and security across multiple settings. With the advent of deep learning (DL), a specialization within machine learning (ML), these systems have been significantly augmented to facilitate DL-based video surveillance services with notable precision. Nevertheless, DL-based [...] Read more.
Video surveillance systems are integral to bolstering safety and security across multiple settings. With the advent of deep learning (DL), a specialization within machine learning (ML), these systems have been significantly augmented to facilitate DL-based video surveillance services with notable precision. Nevertheless, DL-based video surveillance services, which necessitate the tracking of object movement and motion tracking (e.g., to identify unusual object behaviors), can demand a significant portion of computational and memory resources. This includes utilizing GPU computing power for model inference and allocating GPU memory for model loading. To tackle the computational demands inherent in DL-based video surveillance, this study introduces a novel video surveillance management system designed to optimize operational efficiency. At its core, the system is built on a two-tiered edge computing architecture (i.e., client and server through socket transmission). In this architecture, the primary edge (i.e., client side) handles the initial processing tasks, such as object detection, and is connected via a Universal Serial Bus (USB) cable to the Closed-Circuit Television (CCTV) camera, directly at the source of the video feed. This immediate processing reduces the latency of data transfer by detecting objects in real time. Meanwhile, the secondary edge (i.e., server side) plays a vital role by hosting a dynamically controlling threshold module targeted at releasing DL-based models, reducing needless GPU usage. This module is a novel addition that dynamically adjusts the threshold time value required to release DL models. By dynamically optimizing this threshold, the system can effectively manage GPU usage, ensuring resources are allocated efficiently. Moreover, we utilize federated learning (FL) to streamline the training of a Long Short-Term Memory (LSTM) network for predicting imminent object appearances by amalgamating data from diverse camera sources while ensuring data privacy and optimized resource allocation. Furthermore, in contrast to the static threshold values or moving average techniques used in previous approaches for the controlling threshold module, we employ a Deep Q-Network (DQN) methodology to manage threshold values dynamically. This approach efficiently balances the trade-off between GPU memory conservation and the reloading latency of the DL model, which is enabled by incorporating LSTM-derived predictions as inputs to determine the optimal timing for releasing the DL model. The results highlight the potential of our approach to significantly improve the efficiency and effective usage of computational resources in video surveillance systems, opening the door to enhanced security in various domains. Full article
(This article belongs to the Special Issue Artificial Intelligence Methods for Smart Cities—2nd Edition)
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