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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 July 2025 | Viewed by 6125

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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

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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 (4 papers)

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Research

22 pages, 839 KiB

Open AccessArticle

Multi-Agent Reinforcement Learning-Based Routing and Scheduling Models in Time-Sensitive Networking for Internet of Vehicles Communications Between Transportation Field Cabinets

by Sergi Garcia-Cantón, Carlos Ruiz de Mendoza, Cristina Cervelló-Pastor and Sebastià Sallent

Appl. Sci. 2025, 15(3), 1122; https://doi.org/10.3390/app15031122 - 23 Jan 2025

Cited by 2 | Viewed by 1599

Abstract

Future autonomous vehicles will interact with traffic infrastructure through roadside units (RSUs) directly connected to transportation field cabinets (TFCs). These TFCs must be interconnected to share traffic information, enabling infrastructure-to-infrastructure (I2I) communications that are reliable, synchronous and capable of transmitting vehicle data to the Internet. However, I2I communications present a complex optimization challenge. This study addresses this by proposing the design, implementation, and evaluation of an automated management model for I2I service channels based on multi-agent reinforcement learning (MARL) integrated with deep reinforcement learning (DRL). The proposed models efficiently manage the routing and scheduling of data frames between internet of vehicles (IoV) infrastructure devices through time-sensitive networking (TSN) to ensure real-time synchronous I2I communications. The solution incorporates both a routing model and a scheduling model, evaluated in a simulated shared environment where agents operate within the TSN control plane. Both models are tested for different topologies and background traffic levels. The results demonstrate that the models establish the majority of paths in the scenario, adhering to near-optimal routing and scheduling policies. Recursively, for each individual request to create a service channel, the system establishes online an optimal synchronous path between entities with a limited time budget. In total, 71% of optimal routing paths are established and 97% of optimal schedules are achieved. The approach takes into account the periodic nature of the transmitted data and its robustness through TSN networks, obtaining 99 percent of compliant service requests with flow jitter levels below 100 microseconds for different topologies and different network utility percentages. The proposed solution achieves lower execution delays compared to the iterative ILP approach. Additionally, the solution facilitates the integration of 5G networks for vehicle-to-infrastructure (V2I) communications, which is identified as an area for future exploration. Full article

(This article belongs to the Special Issue Novel Advances in Internet of Vehicles)

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32 pages, 5733 KiB

Open AccessArticle

Integrating Visible Light Communication and AI for Adaptive Traffic Management: A Focus on Reward Functions and Rerouting Coordination

by Manuela Vieira, Gonçalo Galvão, Manuel A. Vieira, Mário Vestias, Paula Louro and Pedro Vieira

Appl. Sci. 2025, 15(1), 116; https://doi.org/10.3390/app15010116 - 27 Dec 2024

Cited by 1 | Viewed by 1647

Abstract

This study combines Visible Light Communication (VLC) and Artificial Intelligence (AI) to optimize traffic signal control, reduce congestion, and enhance safety. Utilizing existing road infrastructure, VLC technology transmits real-time data on vehicle and pedestrian positions, speeds, and queues. AI agents, powered by Deep Reinforcement Learning (DRL), process these data to manage traffic flows dynamically, applying anti-bottlenecking and rerouting techniques. A global agent coordinates local agents, enabling indirect communication and a unified DRL model that adjusts traffic light phases in real time using a queue/request/response system. A key focus of this work is the design of reward functions for standard and rerouting scenarios. In standard scenarios, the reward function prioritizes wide green bands for vehicles while penalizing pedestrian rule violations, balancing efficiency and safety. In rerouting scenarios, it dynamically prevents queuing spillovers at neighboring intersections, mitigating cascading congestion and ensuring safe, timely pedestrian crossings. Simulation experiments in the SUMO urban mobility simulator and real-world trials validate the system across diverse intersection types, including four-way crossings, T-intersections, and roundabouts. Results show significant reductions in vehicle and pedestrian waiting times, particularly in rerouting scenarios, demonstrating the system’s scalability and adaptability. By integrating VLC technology and AI-driven adaptive control, this approach achieves efficient, safe, and flexible traffic management. The proposed system addresses urban mobility challenges effectively, offering a robust solution to modern traffic demands while improving the travel experience for all road users. Full article

(This article belongs to the Special Issue Novel Advances in Internet of Vehicles)

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15 pages, 4987 KiB

Open AccessArticle

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 942

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 988

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 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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