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Novel Advances in Internet of Vehicles
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Novel Advances in Internet of Vehicles

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

Deadline for manuscript submissions: 20 December 2024 | Viewed by 1735

Special Issue Editors

Dr. Minghui Liwang

E-Mail Website
Guest Editor
College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
Interests: multi-agent system optimization and decision-making; smart Internet of Things; collaborative planning of drones/unmanned vehicles
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yuliang Tang

E-Mail Website
Guest Editor
School of Informatics, Xiamen University, Xiamen 361005, China
Interests: Internet of Vehicles; network slicing; network function virtualization; MAC protocol

Special Issue Information

Dear Colleagues,

The Internet of Vehicles (IoV) represents an innovative and emerging field which aims to achieve the integration of vehicles with the Internet and other advanced technologies, enabling for advanced communication, data sharing, and automation to improve daily transportation safety, efficiency, and convenience. Therefore, this Special Issue is intended for the presentation of new ideas and experimental results for Advances in the Internet of Vehicles from design, service, and theory to architecture and applications. Areas relevant to Advances in the Internet of Vehicles include, but are not limited to: advanced communication technology-enabled connectivity, autonomous driving and advanced driver assistance systems, novel concurrent algorithms and applications, edge/cloud-assisted IoV, large-scale network management, mobile health care, IoV ecosystem and environmental impact analysis, IoV security, artificial intelligence (AI) and machine learning such as explainable AI, and other sources. In addition, the IoV necessary to achieve high performance and techniques for resource sharing market, in the context of parallel and distributed systems, resource and service trading, and cost-effective and energy-aware transportation, are also topics of interest.

Dr. Minghui Liwang
Prof. Dr. Yuliang Tang
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. 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

  • 5G/6G vehicle-to-everything communications
  • distributed and advanced learning in the IoV
  • space–air–ground-integrated IoV
  • advanced technologies in secure IoV
  • IoV ecosystem and environmental benefits
  • device/edge/cloud computing in the IoV
  • autonomous driving

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Published Papers (2 papers)

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15 pages, 4987 KiB  
Article
End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms
by Jeongho Kim, Joonho Seon, Soohyun Kim, Seongwoo Lee, Jinwook Kim, Byungsun Hwang, Youngghyu Sun and Jinyoung Kim
Appl. Sci. 2024, 14(23), 10832; https://doi.org/10.3390/app142310832 - 22 Nov 2024
Viewed by 344
Abstract
An unmanned aerial vehicle (UAV) swarm has emerged as a powerful tool for mission execution in a variety of applications supported by deep neural networks (DNNs). In the context of UAV swarms, conventional methods for efficient data processing involve transmitting data to cloud [...] Read more.
An unmanned aerial vehicle (UAV) swarm has emerged as a powerful tool for mission execution in a variety of applications supported by deep neural networks (DNNs). In the context of UAV swarms, conventional methods for efficient data processing involve transmitting data to cloud and edge servers. However, these methods often face limitations in adapting to real-time applications due to the low latency of cloud-based approaches and weak mobility of edge-based approaches. In this paper, a new system called deep reinforcement learning-based resilient layer distribution (DRL-RLD) for distributed inference is designed to minimize end-to-end latency in UAV swarm, considering the resource constraints of UAVs. The proposed system dynamically allocates CNN layers based on UAV-to-UAV and UAV-to-ground communication links to minimize end-to-end latency. It can also enhance resilience to maintain mission continuity by reallocating layers when inoperable UAVs occur. The performance of the proposed system was verified through simulations in terms of latency compared to the comparison baselines, and its robustness was demonstrated in the presence of inoperable UAVs. Full article
(This article belongs to the Special Issue Novel Advances in Internet of Vehicles)
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23 pages, 839 KiB  
Article
Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems
by Jinxian Zhao, Dongfang Jiang, Heng Wei, Bingjie Liu, Yifeng Zhao, Yi Zhang, Haoyuan Yu and Xuewei Liu
Appl. Sci. 2024, 14(9), 3713; https://doi.org/10.3390/app14093713 - 26 Apr 2024
Viewed by 679
Abstract
Hybrid beamforming (HBF) has been regarded as one of the most promising technologies in millimeter Wave (mmWave) communication systems. In order to guarantee the communication quality in non-line-of-sight (NLOS) scenarios, joint HBF design for the mmWave amplify-and-forward (AF) relay communication system is studied [...] Read more.
Hybrid beamforming (HBF) has been regarded as one of the most promising technologies in millimeter Wave (mmWave) communication systems. In order to guarantee the communication quality in non-line-of-sight (NLOS) scenarios, joint HBF design for the mmWave amplify-and-forward (AF) relay communication system is studied in this paper. The ideal case is first considered where the mmWave half-duplex (HD) AF relay system operates with channel state information (CSI) accurately known. In order to tackle the non-convex problem, a manifold optimization (MO)-based alternating optimization algorithm is proposed, where an optimization problem containing only constant modulus constraints in Euclidean space can be converted to an unconstrained optimization problem in a Riemann manifold. Furthermore, considering more practical cases with estimation errors of CSI, we investigate the robust joint HBF design with the system operating in full-duplex (FD) mode to obtain higher spectral efficiency (SE). A null-space projection (NP) based self-interference cancellation (SIC) algorithm is developed to attenuate the self-interference (SI). Different from the traditional SI suppression algorithm, there’s no limit on the number of RF chains. Numerical results reveal that our proposed algorithms has a good convergence and can effectively deal with the influence of different CSI estimation errors. A significant performance improvement can be achieved in contrast with other approaches. Full article
(This article belongs to the Special Issue Novel Advances in Internet of Vehicles)
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