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Cyber-Physical Systems for Intelligent Transportation Systems
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Cyber-Physical Systems for Intelligent Transportation Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 4531

Special Issue Editor

Dr. Jie Lin

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China
Interests: cloud computing; Internet of Things

Special Issue Information

Dear Colleagues,

Cyber-Physical Systems for Intelligent Transportation Systems is referred to as the system that can efficiently integrate both cyber and physical components through the integration of the modern computing and communication

Technologies for smart transportation. This poses new challenges in the advancement of computer and communication technologies as well as regarding software solutions. Therefore, this Special Issue is intended for the presentation of new ideas and experimental results in the field of perception, computation and communication of CPS for ITS from design, service, and theory to its practical use.

This Special Issue will publish high-quality, original research papers, in the overlapping fields of:

  • Cyber-Physical Systems for Intelligent Transportation Systems;
  • Parallel and distributed computing;
  • Artificial intelligence, machine learning and deep learning;
  • Computational and data science;
  • Big data applications, algorithms, and systems;
  • Vehicular networks;
  • Cloud/edge/fog computing;
  • Other algorithms, theory and applications related with CPS for ITS;

Dr. Jie Lin
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. Applied Sciences 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 2400 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

  • cyber-physical systems for intelligent transportation systems
  • vehicular network
  • artificial intelligence, machine learning and deep learning
  • edge/cloud computing
  • application specific, reconfigurable, IoT, mobile and embedded architectures
  • architecture modelling and performance evaluation
  • machine learning systems, including algorithms/system co-design

Published Papers (3 papers)

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Research

14 pages, 1005 KiB  
Article
Three Processor Allocation Approaches towards EDF Scheduling for Performance Asymmetric Multiprocessors
by Peng Wu, Zhi Li, Tao Yan and Yingchun Li
Appl. Sci. 2023, 13(9), 5318; https://doi.org/10.3390/app13095318 - 24 Apr 2023
Cited by 3 | Viewed by 1194
Abstract
With the rapid development of high-performance computing and parallel computing technology, by virtue of its cost-effectiveness, strong scalability, and easy programming, the multiprocessor system has gradually become the mainstream computing platform. Meanwhile, a growing number of researchers pay attention to the performance of [...] Read more.
With the rapid development of high-performance computing and parallel computing technology, by virtue of its cost-effectiveness, strong scalability, and easy programming, the multiprocessor system has gradually become the mainstream computing platform. Meanwhile, a growing number of researchers pay attention to the performance of multiprocessor systems, especially the task scheduling problem, which has an important impact on the system performance. Most of the current research works on task scheduling algorithms are based on the homogeneous computing environment. On the contrary, research works focusing on more complex performance asymmetric multiprocessor environments still remain rare. In this paper, we compare the effects of three earliest deadline first algorithms under different processor allocation strategies on performance asymmetric multiprocessors. We propose an efficient schedulability analysis for an allocation strategy that assigns high-priority tasks to the slowest idle processor. Experimental results show that the strategy of allocating processors with optimum speeds for high-priority tasks can schedule more task sets than the other two allocation strategies. The strategy that prioritizes the slowest processors for high-priority tasks has the smallest number of task migrations, and the strategy has the highest effective processor utilization. Full article
(This article belongs to the Special Issue Cyber-Physical Systems for Intelligent Transportation Systems)
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17 pages, 25735 KiB  
Article
POMIC: Privacy-Preserving Outsourcing Medical Image Classification Based on Convolutional Neural Network to Cloud
by Qing Yu, Hanlin Zhang, Hansong Xu and Fanyu Kong
Appl. Sci. 2023, 13(6), 3439; https://doi.org/10.3390/app13063439 - 8 Mar 2023
Viewed by 1171
Abstract
In the medical field, with the increasing number of medical images, medical image classification has become a hot spot. The convolutional neural network, a technology that can process more images and extract more accurate features with nonlinear models, has been widely used in [...] Read more.
In the medical field, with the increasing number of medical images, medical image classification has become a hot spot. The convolutional neural network, a technology that can process more images and extract more accurate features with nonlinear models, has been widely used in this field. However, the classification process with model training with existing medical images needs a large number of samples, and the operation involves complex parameter computations, which puts forward higher requirements for users. Therefore, we propose a scheme for flexible privacy-preserving outsourcing medical image classification based on a convolutional neural network to the cloud. In this paper, three servers on the cloud platform can train the model with images from users, but they cannot obtain complete information on model parameters and user input. In practice, the scheme can not only reduce the computation and storage burdens on the user side but also ensure the security and efficiency of the system, which can be confirmed through the implementation of the experiment. Full article
(This article belongs to the Special Issue Cyber-Physical Systems for Intelligent Transportation Systems)
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20 pages, 6980 KiB  
Article
COPP-DDPG: Computation Offloading with Privacy Preservation in a Vehicular Edge Network
by Yancong Wang, Jian Wang, Hongchang Ke and Zemin Sun
Appl. Sci. 2022, 12(24), 12522; https://doi.org/10.3390/app122412522 - 7 Dec 2022
Viewed by 1477
Abstract
Vehicular edge computing (VEC) is emerging as a prospective technology in the era of 5G and beyond to support delay-sensitive and computation-intensive vehicular applications. However, designing an efficient approach for joint computation offloading and resource allocation is challenging due to the limited resources [...] Read more.
Vehicular edge computing (VEC) is emerging as a prospective technology in the era of 5G and beyond to support delay-sensitive and computation-intensive vehicular applications. However, designing an efficient approach for joint computation offloading and resource allocation is challenging due to the limited resources of VEC servers, the highly dynamic vehicular networks (VNs), different priorities of vehicular applications, and the threat of privacy disclosure. In this work, we propose a cooperative optimization for privacy-preserving and priority-aware offloading and resource allocation in VEC network (VECN) based on deep reinforcement learning (DRL). Firstly, we employed a privacy-preserving framework where the certificate authority (CA) is integrated into the VEC architecture. Furthermore, we formulated the dynamic optimization problem as a Markov decision process (MDP) by constructing a weighted cost function that integrates the priority of stochastic arrival tasks, privacy-preserving of offloading, and dynamic interaction between the edge servers and intelligent connected vehicles (ICVs). To solve this problem, a cooperative optimization for privacy and priority based on deep deterministic policy gradient (COPP-DDPG) is proposed by learning the optimal actions to minimize the weighted cost function. The simulation results show that COPP-DDPG has good convergence and outperforms the other four comparison algorithms in many aspects. Full article
(This article belongs to the Special Issue Cyber-Physical Systems for Intelligent Transportation Systems)
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