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Sensors
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AI and Sensors in Smart Cities
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AI and Sensors in Smart Cities

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

Deadline for manuscript submissions: 1 July 2024 | Viewed by 2092

Special Issue Editors

Prof. Eugenio Di Sciascio

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Guest Editor
Polytechnic University of Bari, 4 Via Orabona, I-70125 Bari, Italy
Interests: information systems; artificial intelligence; cyber-physical systems; mobile and ubiquitous web
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tommaso Di Noia

E-Mail Website
Guest Editor
Department of Electrical and Information Engineering, Polytechnic of Bari, 70125 Bari, Italy
Interests: big data and semantic technologies; artificial intelligence and machine learning; recommender systems; cybersecurity
Special Issues, Collections and Topics in MDPI journals
Prof. Luigi Ranieri

E-Mail Website
Guest Editor
Department of Management, Finance and Technology, University LUM "Giuseppe Degennaro", Bari, Italy
Interests: project management; risk management; smart cities; public service management; energy management

Special Issue Information

Dear Colleagues,

Smart cities, driven by the increasing presence of digital technology, are becoming interconnected systems that can provide their users with increasingly modern and technologically advanced services.

Through the use of Internet of Things (IoT) sensors and the data they provide, the amount of data that can be processed using Machine Learning (ML) algorithms is increasing. The adoption of IoT sensors is allowing cities to manage their services intelligently and to offer new services.

One approach to efficiently manage this large volume of data and the IoT devices and sensors used in smart cities is through the adoption of Artificial Intelligence algorithms.

Therefore, significant research is needed to explore how the Artificial Intelligence approach can be adopted more efficiently in smart cities to offer new services and improve the existing approaches.

This Special Issue will provide a forum for high-quality contributions on the use of Artificial Intelligence in the service of smart cities.

In particular, it will provide a contribution to new ways of acquiring and processing data captured through IoT sensors and subsequent processing using ML algorithms.

This contribution will be crucial for all decision support systems, which are often at the center of the processes governing smart cities. Reviews and investigations on the topic are also welcome.

Prof. Eugenio Di Sciascio
Prof. Dr. Tommaso Di Noia
Prof. Luigi Ranieri
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

  • smart cities
  • artificial intelligence
  • big data analytics
  • IoT and smart applications
  • urban data

Published Papers (2 papers)

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Research

22 pages, 9202 KiB  
Article
Real-Time Three-Dimensional Pedestrian Localization System Using Smartphones
by Beomju Shin, Taehun Kim and Taikjin Lee
Sensors 2024, 24(2), 652; https://doi.org/10.3390/s24020652 - 19 Jan 2024
Viewed by 558
Abstract
Robust and accurate three-dimensional localization is essential for personal navigation, emergency rescue, and worker monitoring in indoor environments. For localization technology to be employed in various applications, it is necessary to reduce infrastructure dependence and limit the maximum error bound. This study aims [...] Read more.
Robust and accurate three-dimensional localization is essential for personal navigation, emergency rescue, and worker monitoring in indoor environments. For localization technology to be employed in various applications, it is necessary to reduce infrastructure dependence and limit the maximum error bound. This study aims to accurately estimate the location of various people using smartphones in a building with a cloud platform-based localization system. The proposed technology is modularized in a hierarchical structure to sequentially estimate the floor and location. This system comprises four localization modules: course level detection, fine level detection (FLD), fine location tracking (FLT), and level change detection (LCD). Each module operates organically according to the current user status. The position estimation range is defined as a total of three phases, and an appropriate location estimation module suitable for the corresponding phase operates to estimate the user’s location gradually and precisely. When the user’s floor is determined by an FLD, the two-dimensional position of the user is estimated by an FLT module that tracks the user’s position by comparing the received signal strength indicator vector sequence and radio map. Also, LCD recognizes the user’s floor change and converts the user’s phase. To verify the proposed technology, various experiments were conducted in a six-story building, and an average accuracy of less than 2 m was obtained. Full article
(This article belongs to the Special Issue AI and Sensors in Smart Cities)
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13 pages, 4069 KiB  
Article
Deep Learning and Geometry Flow Vector Using Estimating Vehicle Cuboid Technology in a Monovision Environment
by Byeongjoon Noh, Tengfeng Lin, Sungju Lee and Taikyeong Jeong
Sensors 2023, 23(17), 7504; https://doi.org/10.3390/s23177504 - 29 Aug 2023
Viewed by 1235
Abstract
This study introduces a novel model for accurately estimating the cuboid of a road vehicle using a monovision sensor and road geometry information. By leveraging object detection models and core vectors, the proposed model overcomes the limitations of multi-sensor setups and provides a [...] Read more.
This study introduces a novel model for accurately estimating the cuboid of a road vehicle using a monovision sensor and road geometry information. By leveraging object detection models and core vectors, the proposed model overcomes the limitations of multi-sensor setups and provides a cost-effective solution. The model demonstrates promising results in accurately estimating cuboids by utilizing the magnitudes of core vectors and considering the average ratio of distances. This research contributes to the field of intelligent transportation by offering a practical and efficient approach to 3D bounding box estimation using monovision sensors. We validated feasibility and applicability are through real-world road images captured by CCTV cameras. Full article
(This article belongs to the Special Issue AI and Sensors in Smart Cities)
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