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Electronics
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Intelligent Technologies for Vehicular Networks
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Intelligent Technologies for Vehicular Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 11189

Special Issue Editor

Prof. Dr. Yolanda Blanco Fernández

E-Mail Website
Guest Editor
atlanTTic research Center for Telecommunication Technologies, University of Vigo, 36310 Vigo, Spain.
Interests: semantic reasoning in personalization applications; machine learning techniques; deep learning models for natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent Transport Systems (ITS) have gained momentum in recent years, where research efforts have focused on the Internet of Vehicles (IoV), security and privacy issues in vehicular networks, the exploitation of vehicular clouds to increase the capabilities of nearby vehicles, and the design of new routing protocols to make communications more efficient, combating the constraints of high mobility to avoid intermittent connections, among others. In this area, the adoption of intelligent technologies enables the deployment of smart vehicular systems (in which vehicles can communicate with each other and with the roadside infrastructure), ranging from autonomous vehicles to collaborative advanced driver assistance systems (co-ADAS) that use, for example, video streaming between vehicles to provide a better visual perspective of the road during overtaking manoeuvres, or even to enable vehicle surveillance systems.

The aim of this Special Issue is to present papers that address still-present problems in the use of intelligent technologies in next-generation vehicular networks and even survey papers to identify emerging trends and new research challenges. The topics covered include, but are not limited to: exploration of the possibilities brought by the Internet of Things (IoT) for the design and development of protocols, applications and services for IOV-related devices, together with the benefits of machine learning and deep learning algorithms for the intelligent management of vehicular systems (traffic optimisation, road safety issues, social sensing services, privacy techniques, clustering, localization and detection, allocation of computing resources in cloud-based IoV applications and architectures, etc.).

Prof. Dr. Yolanda Blanco Fernández
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. Electronics 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

  • vehicular networks
  • machine learning
  • vehicle-to-everything (V2X)
  • resource allocation
  • intelligent vehicular systems
  • deep learning
  • recurrent neural networks (RNNs)
  • convolutional neural networks (CNNs)
  • IoT
  • IoV
  • networking
  • cloud-based vehicular technologies

Related Special Issue

Published Papers (9 papers)

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Research

28 pages, 8945 KiB  
Article
Vulnerable Road User Safety Using Mobile Phones with Vehicle-to-VRU Communication
by Sukru Yaren Gelbal, Bilin Aksun-Guvenc and Levent Guvenc
Electronics 2024, 13(2), 331; https://doi.org/10.3390/electronics13020331 - 12 Jan 2024
Viewed by 888
Abstract
Pedestrians, bicyclists, and scooterists are Vulnerable Road Users (VRUs) in traffic accidents. The number of fatalities and injuries in traffic accidents involving vulnerable road users has been steadily increasing in the last two decades in the U.S., even though road vehicles now have [...] Read more.
Pedestrians, bicyclists, and scooterists are Vulnerable Road Users (VRUs) in traffic accidents. The number of fatalities and injuries in traffic accidents involving vulnerable road users has been steadily increasing in the last two decades in the U.S., even though road vehicles now have perception sensors like cameras to detect risk and issue collision warnings or apply emergency braking. Perception sensors like cameras are highly affected by lighting and weather conditions. Cameras, radar, and lidar cannot detect vulnerable road users in partially occluded and occluded situations. This paper proposes the use of Vehicle-to-VRU communication to inform nearby vehicles of VRUs on trajectories with a potential collision risk. An Android smartphone app with low-energy Bluetooth (BLE) advertising is developed and used for this communication. The same app is also used to collect motion data of VRUs for training. VRU motion data are smoothed using a Kalman filter, and an LSTM neural network is used for future motion prediction. This information is used in an algorithm comparing Time-To-collision-Zone (TTZ) for the vehicle and VRU, and issues driver warnings with different severity levels. The warning severity level is based on the analysis of real data from a smart intersection for close vehicle and VRU interactions. The resulting driver warning system is demonstrated using proof-of-concept experiments. The method can easily be extended to a VRU collision-mitigation system. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks)
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18 pages, 2806 KiB  
Article
QoS-Driven Slicing Management for Vehicular Communications
by Prohim Tam, Seyha Ros, Inseok Song and Seokhoon Kim
Electronics 2024, 13(2), 314; https://doi.org/10.3390/electronics13020314 - 10 Jan 2024
Viewed by 904
Abstract
Network slicing is introduced for elastically instantiating logical network infrastructure isolation to support different application types with diversified quality of service (QoS) class indicators. In particular, vehicular communications are a trending area that consists of massive mission-critical applications in the range of safety-critical, [...] Read more.
Network slicing is introduced for elastically instantiating logical network infrastructure isolation to support different application types with diversified quality of service (QoS) class indicators. In particular, vehicular communications are a trending area that consists of massive mission-critical applications in the range of safety-critical, intelligent transport systems, and on-board infotainment. Slicing management can be achieved if the network infrastructure has computing sufficiency, a dynamic control policy, elastic resource virtualization, and cross-tier orchestration. To support the functionality of slicing management, incorporating core network infrastructure with deep learning and reinforcement learning has become a hot topic for researchers and practitioners in analyzing vehicular traffic/resource patterns before orchestrating the steering policies. In this paper, we propose QoS-driven management by considering (edge) resource block utilization, scheduling, and slice instantiation in a three-tier resource placement, namely, small base stations/access points, macro base stations, and core networks. The proposed scheme integrates recurrent neural networks to trigger hidden states of resource availability and predict the output of QoS. The intelligent agent and slice controller, namely, RDQ3N, gathers the resource states from three-tier observations and optimizes the action on allocation and scheduling algorithms. Experiments are conducted on both physical and virtual representational vehicle-to-everything (V2X) environments; furthermore, service requests are set to massive thresholds for rendering V2X congestion flow entries. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks)
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24 pages, 1200 KiB  
Article
Adaptive Truck Platooning with Drones: A Decentralized Approach for Highway Monitoring
by J. de Curtò, I. de Zarzà, Juan Carlos Cano, Pietro Manzoni and Carlos T. Calafate
Electronics 2023, 12(24), 4913; https://doi.org/10.3390/electronics12244913 - 06 Dec 2023
Cited by 1 | Viewed by 877
Abstract
The increasing demand for efficient and safe transportation systems has led to the development of autonomous vehicles and vehicle platooning. Truck platooning, in particular, offers numerous benefits, such as reduced fuel consumption, enhanced traffic flow, and increased safety. In this paper, we present [...] Read more.
The increasing demand for efficient and safe transportation systems has led to the development of autonomous vehicles and vehicle platooning. Truck platooning, in particular, offers numerous benefits, such as reduced fuel consumption, enhanced traffic flow, and increased safety. In this paper, we present a drone-based decentralized framework for truck platooning in highway monitoring scenarios. Our approach employs multiple drones, which communicate with the trucks and make real-time decisions on whether to form a platoon or not, leveraging Model Predictive Control (MPC) and Unscented Kalman Filter (UKF) for drone formation control. The proposed framework integrates a simple truck model in the existing drone-based simulation, addressing the truck dynamics and constraints for practical applicability. Simulation results demonstrate the effectiveness of our approach in maintaining the desired platoon formations while ensuring collision avoidance and adhering to the vehicle constraints. This innovative drone-based truck platooning system has the potential to significantly improve highway monitoring efficiency, traffic management, and safety. Our drone-based truck platooning system is primarily designed for implementation in highway monitoring and management scenarios, where its enhanced communication and real-time decision-making capabilities can significantly contribute to traffic efficiency and safety. Future work may focus on field trials to validate the system in real-world conditions and further refine the algorithms based on practical feedback and evolving vehicular technologies. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks)
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21 pages, 1145 KiB  
Article
Vehicular Localization Framework with UWB and DAG-Based Distributed Ledger for Ensuring Positioning Accuracy and Security
by Kiseok Kim, Sangmin Lee, Taehoon Yoo and Hwangnam Kim
Electronics 2023, 12(23), 4756; https://doi.org/10.3390/electronics12234756 - 23 Nov 2023
Cited by 1 | Viewed by 804
Abstract
In performing various missions using various types of vehicles or other moving objects, the positioning of each agent within the swarm is essential. In particular, for missions that require precise location estimation, the case of malicious attacks through data forgery cannot be excluded. [...] Read more.
In performing various missions using various types of vehicles or other moving objects, the positioning of each agent within the swarm is essential. In particular, for missions that require precise location estimation, the case of malicious attacks through data forgery cannot be excluded. In this paper, we propose a highly secure and accurate localization framework utilizing a Directed-Acyclic-Graph (DAG)-based distributed ledger as an intelligent vehicle network for an Ultra-Wideband (UWB) positioning system. When performing UWB positioning, the data generated from each node are used to calculate the Time of Flight (ToF), and if any of them are tampered with, the overall positioning performance is greatly reduced. We prevented positioning performance degradation by ensuring the safety and integrity of the data by applying a chain-based logical network between each node utilized for UWB positioning. The experimental results indicated that the proposed framework was effective at providing system stability and security without affecting the UWB positioning performance. In addition, the performance of the framework was verified by presenting defense indicators for various attack scenarios. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks)
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11 pages, 966 KiB  
Article
Video Blockchain: A Decentralized Approach for Secure and Sustainable Networks with Distributed Video Footage from Vehicle-Mounted Cameras in Smart Cities
by Kasun Moolikagedara, Minh Nguyen, Wei Qi Yan and Xue Jun Li
Electronics 2023, 12(17), 3621; https://doi.org/10.3390/electronics12173621 - 27 Aug 2023
Cited by 5 | Viewed by 1154
Abstract
In this paper, we explore video blockchain for establishing connectivity among vehicles in a smart city through utilizing blockchain technology. By leveraging intelligent vehicular systems that provide location-based visualization through multiple deployed cameras in vehicles, we expand the scope of collecting video surveillance [...] Read more.
In this paper, we explore video blockchain for establishing connectivity among vehicles in a smart city through utilizing blockchain technology. By leveraging intelligent vehicular systems that provide location-based visualization through multiple deployed cameras in vehicles, we expand the scope of collecting video surveillance data for observation, thereby enhancing overall situational awareness. We utilize the decentralized nature of blockchain to implement a vehicle-based surveillance system across a smart city. To ensure reliability, the integration of two cryptographic functions, hashing and signing, with the blockchain is employed. This integration ensures secure and tamper-proof solutions for the existing intelligent surveillance system. In this paper, our primary focus is on combining blockchain technology to achieve sustainable and robust smart solutions for intelligent vehicular distributed video networks while eliminating the need for third-party intermediaries. Through extensive experiments and analysis, we demonstrate the effectiveness and feasibility of our proposed video blockchain approach. The results indicate that this innovative framework provides enhanced security, privacy, and scalability for intelligent vehicular distributed networks in smart cities, paving the way for a connected and efficient urban environment. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks)
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16 pages, 3813 KiB  
Article
DRL-Based Backbone SDN Control Methods in UAV-Assisted Networks for Computational Resource Efficiency
by Inseok Song, Prohim Tam, Seungwoo Kang, Seyha Ros and Seokhoon Kim
Electronics 2023, 12(13), 2984; https://doi.org/10.3390/electronics12132984 - 06 Jul 2023
Cited by 7 | Viewed by 1306
Abstract
The limited coverage extension of mobile edge computing (MEC) necessitates exploring cooperation with unmanned aerial vehicles (UAV) to leverage advanced features for future computation-intensive and mission-critical applications. Moreover, the workflow for task offloading in software-defined networking (SDN)-enabled 5G is significant to tackle in [...] Read more.
The limited coverage extension of mobile edge computing (MEC) necessitates exploring cooperation with unmanned aerial vehicles (UAV) to leverage advanced features for future computation-intensive and mission-critical applications. Moreover, the workflow for task offloading in software-defined networking (SDN)-enabled 5G is significant to tackle in UAV-MEC networks. In this paper, deep reinforcement learning (DRL) SDN control methods for improving computing resources are proposed. DRL-based SDN controller, termed DRL-SDNC, allocates computational resources, bandwidth, and storage based on task requirements, upper-bound tolerable delays, and network conditions, using the UAV system architecture for task exchange between MECs. DRL-SDNC configures rule installation based on state observations and agent evaluation indicators, such as network congestion, user equipment computational capabilities, and energy efficiency. This paper also proposes the training deep network architecture for the DRL-SDNC, enabling interactive and autonomous policy enforcement. The agent learns from the UAV-MEC environment through experience gathering and updates its parameters using optimization methods. DRL-SDNC collaboratively adjusts hyperparameters and network architecture to enhance learning efficiency. Compared with baseline schemes, simulation results demonstrate the effectiveness of the proposed approach in optimizing resource efficiency and achieving satisfied quality of service for efficient utilization of computing and communication resources in UAV-assisted networking environments. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks)
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28 pages, 4408 KiB  
Article
Intelligent Embedded Systems Platform for Vehicular Cyber-Physical Systems
by Christopher Conrad, Saba Al-Rubaye and Antonios Tsourdos
Electronics 2023, 12(13), 2908; https://doi.org/10.3390/electronics12132908 - 02 Jul 2023
Cited by 1 | Viewed by 2170
Abstract
Intelligent vehicular cyber-physical systems (ICPSs) increase the reliability, efficiency and adaptability of urban mobility systems. Notably, ICPSs enable autonomous transportation in smart cities, exemplified by the emerging fields of self-driving cars and advanced air mobility. Nonetheless, the deployment of ICPSs raises legitimate concerns [...] Read more.
Intelligent vehicular cyber-physical systems (ICPSs) increase the reliability, efficiency and adaptability of urban mobility systems. Notably, ICPSs enable autonomous transportation in smart cities, exemplified by the emerging fields of self-driving cars and advanced air mobility. Nonetheless, the deployment of ICPSs raises legitimate concerns surrounding safety assurance, cybersecurity threats, communication reliability, and data management. Addressing these issues often necessitates specialised platforms to cater to the heterogeneity and complexity of ICPSs. To address this challenge, this paper presents a comprehensive CPS to explore, develop and test ICPSs and intelligent vehicular algorithms. A customisable embedded system is realised using a field programmable gate array, which is connected to a supervisory computer to enable networked operations and support advanced multi-agent algorithms. The platform remains compatible with multiple vehicular sensors, communication protocols and human–machine interfaces, essential for a vehicle to perceive its surroundings, communicate with collaborative systems, and interact with its occupants. The proposed CPS thereby offers a practical resource to advance ICPS development, comprehension, and experimentation in both educational and research settings. By bridging the gap between theory and practice, this tool empowers users to overcome the complexities of ICPSs and contribute to the emerging fields of autonomous transportation and intelligent vehicular systems. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks)
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22 pages, 1981 KiB  
Article
Content Caching and Distribution Policies for Vehicular Ad-Hoc Networks (VANETs): Modeling and Simulation
by Irene Kilanioti, Nikolaos Astrinakis and Symeon Papavassiliou
Electronics 2023, 12(13), 2901; https://doi.org/10.3390/electronics12132901 - 01 Jul 2023
Viewed by 779
Abstract
The paper studies the application of various content distribution policies for vehicular ad hoc networks (VANETs) and compares their effectiveness under various simulation scenarios. Our implementation augments the existing Veins tool, an open source framework for vehicular network simulations based on the discrete [...] Read more.
The paper studies the application of various content distribution policies for vehicular ad hoc networks (VANETs) and compares their effectiveness under various simulation scenarios. Our implementation augments the existing Veins tool, an open source framework for vehicular network simulations based on the discrete event simulator OMNET++ and SUMO, a tool that simulates traffic on road networks. The proposed solution integrates various additional features into the pre-existing Veins realizations and expands them to include the modeling and implementation of proposed caching and content distribution policies and the measurement of respective metrics. Moreover, we integrate machine learning algorithms for distribution policies into the simulation framework in order to efficiently study distribution of content to the network nodes. These algorithms are pre-trained neural network models adapted for VANETs. Using these new functions, we can specify the simulation parameters, run a plethora of experiments and proceed to evaluate metrics and policies for content distribution. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks)
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22 pages, 5806 KiB  
Article
Efficient Route Planning Using Temporal Reliance of Link Quality for Highway IoV Traffic Environment
by Ritesh Yaduwanshi, Sushil Kumar, Arvind Kumar, Omprakash Kaiwartya, Deepti, Mohammad Aljaidi and Jaime Lloret
Electronics 2023, 12(1), 130; https://doi.org/10.3390/electronics12010130 - 28 Dec 2022
Viewed by 1204
Abstract
Intermittently connected vehicular networks, terrain of the highway, and high mobility of the vehicles are the main critical constraints of highway IoV (Internet of Vehicles) traffic environment. These cause GPS outage problem and the existence of short-lived wireless mobile links that reduce the [...] Read more.
Intermittently connected vehicular networks, terrain of the highway, and high mobility of the vehicles are the main critical constraints of highway IoV (Internet of Vehicles) traffic environment. These cause GPS outage problem and the existence of short-lived wireless mobile links that reduce the performance of designed routing approaches. Nevertheless, geographic routing has attracted a lot of attention from researchers as a potential means of accurate and efficient information delivery. Various distance-based routing protocols have been proposed in the literature, with an emphasis on restricting the forwarding area to the next forwarding vehicle. Many of these protocols have issues with significant one-hop link disconnection, long end-to-end delays, and low throughput even at normal vehicle speeds in high-vehicular-density environments due to frequently interrupted wireless links. In this paper, an efficient geocast routing (EGR) approach for highway IoV–traffic environment considering the shadowing fading condition is proposed. In EGR, a geometrical localization for GPS outage problem and a temporal link quality estimation model considering underlying vehicular movement have been proposed. Geocast routing to select a next forwarding vehicle from forward region by utilizing temporal link quality is proposed for four different scenarios. To evaluate the effectiveness and scalability of EGR, a comparative performance evaluation based on simulations has been performed. It is clear from the analysis of the results that EGR performs better than state-of-the-art approaches in highway traffic environment in terms of handling the problem of wireless communication link breakage and throughput, as well as ensuring the faster delivery of the messages. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks)
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