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Artificial Intelligence (AI) and Sensors in Smart City Transportation and Logistics

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

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 9871

Special Issue Editors

Prof. Dr. Wenfeng Li

E-Mail Website
Guest Editor
Department of Logistics Engineering, Wuhan University of Technology, Wuhan, China
Interests: body area networks; internet of things; logistics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gabriel Lodewijks

E-Mail Website
Guest Editor
School of Engineering, College of Engineering, Science and Environment, The University of Newcastle, Newcastle, Australia
Interests: aerospace systems; aviation technology; logistics; transport engineering
Dr. Xiuwen Fu

E-Mail Website
Guest Editor
Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
Interests: Internet of Things; wireless sensor networks; unmanned systems; swarm intelligence; network reliability; network modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Yulian Cao

E-Mail Website
Guest Editor
School of Modern Post, Beijing University of Posts and Telecommunications, Beijing, China
Interests: operational research optimization; logistics optimization; aviation logistics optimization; intelligent optimization algorithm; heuristic algorithm; particle swarm optimization algorithm; cultural gene algorithm; scheduling optimization; logistics system modeling and simulation; logistics location optimization; smart logistics; logistics and supply chain management; Internet of Things and logistics informatization

Special Issue Information

Dear Colleagues,

The emergence of ChatGPT-like products has sparked discussions on AI development and applications, including in intelligent transportation and logistics in urban areas. Examples include unmanned driving, contactless delivery, intelligent warehousing, and touchless passage. New sensors and their combinations play a significant role, and these developments have demonstrated powerful resolution during the COVID-19 pandemic and high potential in an aging society.

This Special Issue, therefore, aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of AI and sensors in smart city transportation and logistics.

Potential topics include but are not limited to:

  • AI and sensors for autonomous systems;
  • Sensor-based artificial intelligence in smart city transportation and logistics;
  • AI-based sensing in smart city transportation and logistics;
  • IoT in smart city transportation and logistics;
  • Eye tracking and neurophysiological technology;
  • Autonomous transport and logistic systems on airports;
  • V2X for smart transportation; 
  • Smart multimodal transportation;
  • Smart harbor;
  • Smart warehouse.

Prof. Dr. Wenfeng Li
Prof. Dr. Gabriel Lodewijks
Dr. Xiuwen Fu
Dr. Yulian Cao
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.

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

Published Papers (4 papers)

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Research

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21 pages, 1089 KiB  
Article
Cloud IaaS Optimization Using Machine Vision at the IoT Edge and the Grid Sensing Algorithm
by Nuruzzaman Faruqui, Sandesh Achar, Sandeepkumar Racherla, Vineet Dhanawat, Prathyusha Sripathi, Md. Monirul Islam, Jia Uddin, Manal A. Othman, Md Abdus Samad and Kwonhue Choi
Sensors 2024, 24(21), 6895; https://doi.org/10.3390/s24216895 - 27 Oct 2024
Cited by 6 | Viewed by 1643
Abstract
Security grids consisting of High-Definition (HD) Internet of Things (IoT) cameras are gaining popularity for organizational perimeter surveillance and security monitoring. Transmitting HD video data to cloud infrastructure requires high bandwidth and more storage space than text, audio, and image data. It becomes [...] Read more.
Security grids consisting of High-Definition (HD) Internet of Things (IoT) cameras are gaining popularity for organizational perimeter surveillance and security monitoring. Transmitting HD video data to cloud infrastructure requires high bandwidth and more storage space than text, audio, and image data. It becomes more challenging for large-scale organizations with massive security grids to minimize cloud network bandwidth and storage costs. This paper presents an application of Machine Vision at the IoT Edge (Mez) technology in association with a novel Grid Sensing (GRS) algorithm to optimize cloud Infrastructure as a Service (IaaS) resource allocation, leading to cost minimization. Experimental results demonstrated a 31.29% reduction in bandwidth and a 22.43% reduction in storage requirements. The Mez technology offers a network latency feedback module with knobs for transforming video frames to adjust to the latency sensitivity. The association of the GRS algorithm introduces its compatibility in the IoT camera-driven security grid by automatically ranking the existing bandwidth requirements by different IoT nodes. As a result, the proposed system minimizes the entire grid’s throughput, contributing to significant cloud resource optimization. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Sensors in Smart City Transportation and Logistics)
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11 pages, 2027 KiB  
Communication
Optimal Control for a Superconducting Hybrid MagLev Transport System with Multirate Multisensors in a Smart Factory
by Changhyun Kim
Sensors 2024, 24(2), 671; https://doi.org/10.3390/s24020671 - 21 Jan 2024
Cited by 1 | Viewed by 1254
Abstract
Recently, magnetic levitation systems have been applied and studied in various industrial fields. In particular, in-tracktype magnetic levitation conveyor systems are actively studied since they can effectively minimize electromagnetic effects in processes that require a highly clean environment. In this type of system, [...] Read more.
Recently, magnetic levitation systems have been applied and studied in various industrial fields. In particular, in-tracktype magnetic levitation conveyor systems are actively studied since they can effectively minimize electromagnetic effects in processes that require a highly clean environment. In this type of system, diverse and multiple sensors are structurally required so that the control performance of an integrated system is primarily governed by the slowest measuring sensor. This paper proposes a multisensor fusion compensator to integrate the outputs obtained from various sensors into one output with the single fastest time rate. Since the state of the system is estimated at a fast time rate, the optimal controller also guarantees fast performance and stability. The computation of electromagnetic fields and the control performance of the considered superconducting hybrid system were analyzed using a computer simulation based on finite element methods. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Sensors in Smart City Transportation and Logistics)
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21 pages, 6237 KiB  
Article
CNN-Based Vehicle Bottom Face Quadrilateral Detection Using Surveillance Cameras for Intelligent Transportation Systems
by Gahyun Kim, Ho Gi Jung and Jae Kyu Suhr
Sensors 2023, 23(15), 6688; https://doi.org/10.3390/s23156688 - 26 Jul 2023
Cited by 3 | Viewed by 1790
Abstract
In intelligent transportation systems, it is essential to estimate the vehicle position accurately. To this end, it is preferred to detect vehicles as a bottom face quadrilateral (BFQ) rather than an axis-aligned bounding box. Although there have been some methods for detecting the [...] Read more.
In intelligent transportation systems, it is essential to estimate the vehicle position accurately. To this end, it is preferred to detect vehicles as a bottom face quadrilateral (BFQ) rather than an axis-aligned bounding box. Although there have been some methods for detecting the vehicle BFQ using vehicle-mounted cameras, few studies have been conducted using surveillance cameras. Therefore, this paper conducts a comparative study on various approaches for detecting the vehicle BFQ in surveillance camera environments. Three approaches were selected for comparison, including corner-based, position/size/angle-based, and line-based. For comparison, this paper suggests a way to implement the vehicle BFQ detectors by simply adding extra heads to one of the most widely used real-time object detectors, YOLO. In experiments, it was shown that the vehicle BFQ can be adequately detected by using the suggested implementation, and the three approaches were quantitatively evaluated, compared, and analyzed. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Sensors in Smart City Transportation and Logistics)
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Review

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39 pages, 3715 KiB  
Review
Understanding the Role of Sensor Optimisation in Complex Systems
by Burak Suslu, Fakhre Ali and Ian K. Jennions
Sensors 2023, 23(18), 7819; https://doi.org/10.3390/s23187819 - 12 Sep 2023
Cited by 4 | Viewed by 3706
Abstract
Complex systems involve monitoring, assessing, and predicting the health of various systems within an integrated vehicle health management (IVHM) system or a larger system. Health management applications rely on sensors that generate useful information about the health condition of the assets; thus, optimising [...] Read more.
Complex systems involve monitoring, assessing, and predicting the health of various systems within an integrated vehicle health management (IVHM) system or a larger system. Health management applications rely on sensors that generate useful information about the health condition of the assets; thus, optimising the sensor network quality while considering specific constraints is the first step in assessing the condition of assets. The optimisation problem in sensor networks involves considering trade-offs between different performance metrics. This review paper provides a comprehensive guideline for practitioners in the field of sensor optimisation for complex systems. It introduces versatile multi-perspective cost functions for different aspects of sensor optimisation, including selection, placement, data processing and operation. A taxonomy and concept map of the field are defined as valuable navigation tools in this vast field. Optimisation techniques and quantification approaches of the cost functions are discussed, emphasising their adaptability to tailor to specific application requirements. As a pioneering contribution, all the relevant literature is gathered and classified here to further improve the understanding of optimal sensor networks from an information-gain perspective. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Sensors in Smart City Transportation and Logistics)
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