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Applied Sciences
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Vehicular Edge Computing and Networking
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Vehicular Edge Computing and Networking

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (10 June 2023) | Viewed by 11685

Special Issue Editors

Dr. Pedro Rito

E-Mail Website
Guest Editor
Institute of Telecommunications, University of Aveiro, Aveiro, Portugal
Interests: wireless networks; radio access networks; software-defined radio; software-defined networking; vehicular communications; smart cities
Prof. Dr. Susana Sargento

E-Mail Website
Guest Editor
Instituto de Telecomunicações, University of Aveiro, Aveiro, Portugal
Interests: heterogeneous networks; architectures for the future internet; vehicular ad-hoc and mesh networks; cognitive and self-management networks; virtualization

Special Issue Information

Dear Colleagues,

We are inviting submissions to this Special Issue entitled Vehicular Edge Computing and Networking.

With the advent of intelligent transportation systems (ITS), which include vehicular ad-hoc networks (VANET) and smart cities connecting all parts of an urban environment, vehicles are part of the network by publishing and receiving information to and from other vehicles, infrastructure such as road side units (RSUs) and vulnerable road users (VRUs)—overall called V2X. RSUs can be connected to the internet and, through it, to the cloud. They can also play the role of edge devices if they have computing power—vehicular edge computing (VEC). VEC can provide and manage computational resources closer to vehicles and end-users, providing access to services at a lower latency and meeting the minimum execution requirements for services with geotemporal requirements.

Vehicular networks can have variable density in urban centers, surroundings, and highways. Their most common applications are related to safety and entertainment. Road traffic causes a series of accidents that can injure passengers. Some applications increase the ease and safety of driving (defining routes, eliminating traffic congestion, notifying the presence of obstacles) and may limit the number of accidents. Moreover, with the introduction of autonomous driving solutions, avoiding dangerous road situations and protecting VRUs is more critical than ever. VEC and VANET could also enhance entertainment services experience in vehicles (such as video streaming) for passenger convenience.

In the context of vehicular networks, in addition to different types of applications, there are aspects related to the dispatch and routing of data packets, the quality of service and experience, and security and privacy, which still pose several challenges.

Within the framework of this Special Issue, we invite different researchers to write manuscripts focused on, but not limited to, the topics described above.

Dr. Pedro Rito
Prof. Dr. Susana Sargento
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

  • vehicular edge computing
  • multi-access edge computing
  • vehicular ad hoc networks
  • vehicular-software-defined networking
  • vehicular edge computing applications
  • security and privacy in vehicular edge computing
  • computation offloading in vehicular edge computing
  • resource alocation and caching
  • business models for vehicular edge computing

Published Papers (6 papers)

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Research

19 pages, 997 KiB  
Article
Vehicular Edge-Computing Framework for Making Use of Parking and Charging Electric Vehicles
by Qi Deng and Feng Zeng
Appl. Sci. 2023, 13(6), 4065; https://doi.org/10.3390/app13064065 - 22 Mar 2023
Cited by 1 | Viewed by 1438
Abstract
In big cities, there are more and more parking lots and charging piles for electric vehicles, and the resources of parking and charging vehicles can be aggregated to provide strong computing power for vehicular edge computing (VEC). In this paper, we propose a [...] Read more.
In big cities, there are more and more parking lots and charging piles for electric vehicles, and the resources of parking and charging vehicles can be aggregated to provide strong computing power for vehicular edge computing (VEC). In this paper, we propose a VEC framework that uses charging vehicles in parking lots to assist edge servers in processing computational tasks, and an edge crowdsourcing platform (ECP) is designed to manage and integrate the idle computation resources of electric vehicles in parking lots to provide computation services for requesting vehicles. Based on game theory, we first model the interactions among the edge server, the ECP and the requesting vehicles as a Stackelberg game, and theoretically prove the existence of a Nash equilibrium for this Stackelberg game. Then, a genetic algorithm-based game-strategy solving algorithm is proposed to find the optimal strategy for the edge server and ECP. The simulation results demonstrate that the performance of our proposed solution is better than other traditional solutions. Compared with the solution without ECP, our solution can increase the utilities of the edge server and the requesting vehicle by 13.3% and 10.99%, respectively. Full article
(This article belongs to the Special Issue Vehicular Edge Computing and Networking)
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21 pages, 778 KiB  
Article
A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET
by Xiaofeng Liu, Ben St. Amour and Arunita Jaekel
Appl. Sci. 2023, 13(6), 3640; https://doi.org/10.3390/app13063640 - 13 Mar 2023
Cited by 12 | Viewed by 2003
Abstract
Vehicular ad hoc networks (VANETs) are crucial components of intelligent transportation systems (ITS) aimed at enhancing road safety and providing additional services to vehicles and their users. To achieve reliable delivery of periodic status information, referred to as basic safety messages (BSMs) and [...] Read more.
Vehicular ad hoc networks (VANETs) are crucial components of intelligent transportation systems (ITS) aimed at enhancing road safety and providing additional services to vehicles and their users. To achieve reliable delivery of periodic status information, referred to as basic safety messages (BSMs) and event-driven alerts, vehicles need to manage the conflicting requirements of situational awareness and congestion control in a dynamic environment. To address this challenge, this paper focuses on controlling the message transmission rate through a Markov decision process (MDP) and solves it using a novel reinforcement learning (RL) algorithm. The proposed RL approach selects the most suitable transmission rate based on the current channel conditions, resulting in a balanced performance in terms of packet delivery and channel congestion, as shown by simulation results for different traffic scenarios. Additionally, the proposed approach offers increased flexibility for adaptive congestion control through the design of an appropriate reward function. Full article
(This article belongs to the Special Issue Vehicular Edge Computing and Networking)
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17 pages, 1743 KiB  
Article
Embedded Federated Learning for VANET Environments
by Renato Valente, Carlos Senna, Pedro Rito and Susana Sargento
Appl. Sci. 2023, 13(4), 2329; https://doi.org/10.3390/app13042329 - 11 Feb 2023
Cited by 2 | Viewed by 1655
Abstract
In the scope of smart cities, the sensors scattered throughout the city generate information that supplies intelligence mechanisms to learn the city’s mobility patterns. These patterns are used in machine learning (ML) applications, such as traffic estimation, that allow for improvement in the [...] Read more.
In the scope of smart cities, the sensors scattered throughout the city generate information that supplies intelligence mechanisms to learn the city’s mobility patterns. These patterns are used in machine learning (ML) applications, such as traffic estimation, that allow for improvement in the quality of experience in the city. Owing to the Internet-of-Things (IoT) evolution, the city’s monitoring points are always growing, and the transmission of the mass of data generated from edge devices to the cloud, required by centralized ML solutions, brings great challenges in terms of communication, thus negatively impacting the response time and, consequently, compromising the reaction in improving the flow of vehicles. In addition, when moving between the edge and the cloud, data are exposed, compromising privacy. Federated learning (FL) has emerged as an option for these challenges: (1) It has lower latency and communication overhead when performing most of the processing on the edge devices; (2) it improves privacy, as data do not travel over the network; and (3) it facilitates the handling of heterogeneous data sources and expands scalability. To assess how FL can effectively contribute to smart city scenarios, we present an FL framework, for which we built a testbed that integrated the components of the city infrastructure, where edge devices such as NVIDIA Jetson were connected to a cloud server. We deployed our lightweight container-based FL framework in this testbed, and we evaluated the performance of devices, the effectiveness of ML and aggregation algorithms, the impact on the communication between the edge and the server, and the consumption of resources. To carry out the evaluation, we opted for a scenario in which we estimated vehicle mobility inside and outside the city, using real data collected by the Aveiro Tech City Living Lab communication and sensing infrastructure in the city of Aveiro, Portugal. Full article
(This article belongs to the Special Issue Vehicular Edge Computing and Networking)
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15 pages, 2964 KiB  
Article
Task Offloading Strategy of Vehicular Networks Based on Improved Bald Eagle Search Optimization Algorithm
by Xianhao Shen, Zhaozhan Chang, Xiaolan Xie and Shaohua Niu
Appl. Sci. 2022, 12(18), 9308; https://doi.org/10.3390/app12189308 - 16 Sep 2022
Cited by 5 | Viewed by 1487
Abstract
To reduce computing delay and energy consumption in the Vehicular networks, the total cost of task offloading, namely delay and energy consumption, is studied. A task offloading model combining local vehicle computing, MEC (Mobile Edge Computing) server computing, and cloud computing is proposed. [...] Read more.
To reduce computing delay and energy consumption in the Vehicular networks, the total cost of task offloading, namely delay and energy consumption, is studied. A task offloading model combining local vehicle computing, MEC (Mobile Edge Computing) server computing, and cloud computing is proposed. The model not only considers the priority relationship of tasks, but also considers the delay and energy consumption of the system. A computational offloading decision method IBES based on an improved bald eagle search optimization algorithm is designed, which introduces Tent chaotic mapping, Levy Flight mechanism and Adaptive weights into the bald eagle search optimization algorithm to increase initial population diversity, enhance local search and global convergence. The simulation results show that the total cost of IBES is 33.07% and 22.73% lower than that of PSO and BES, respectively. Full article
(This article belongs to the Special Issue Vehicular Edge Computing and Networking)
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27 pages, 2977 KiB  
Article
Coordinated Multi-Platooning Planning for Resolving Sudden Congestion on Multi-Lane Freeways
by Jia-You Lin, Chia-Che Tsai, Van-Linh Nguyen and Ren-Hung Hwang
Appl. Sci. 2022, 12(17), 8622; https://doi.org/10.3390/app12178622 - 28 Aug 2022
Cited by 1 | Viewed by 1652
Abstract
Resolving traffic congestion caused by sudden events (e.g., an accident, lane closed due to construction) on the freeway has always been a problem that is challenging to address perfectly. The congestion resolution can take hours if the congestion is severe, and the vehicles [...] Read more.
Resolving traffic congestion caused by sudden events (e.g., an accident, lane closed due to construction) on the freeway has always been a problem that is challenging to address perfectly. The congestion resolution can take hours if the congestion is severe, and the vehicles must voluntarily line up to exit the congestion spots. Most state-of-the-art traffic scheduling schemes often rely on traffic signal controllers to mitigate traffic congestion in fixed areas (e.g., intersection, blocked areas). Unlike the existing studies, in this work, we introduce a novel decentralized coordinated platooning planning method, namely Coordinated Platooning Planning (CPP), for quickly resolving temporary traffic congestion in any place on multi-lane freeways heuristically. First, based on warning notifications about traffic congestion, we propose a maneuver control protocol that enables the vehicles to negotiate with surrounding vehicles to determine a consensus plan for forming platoons (who is platoon leader, the value of the distance gap, vehicle velocity, platoon size) in sequential areas. After creating the platoons, each platoon leader commands their platoon members through the maneuver protocol to urge the vehicles to move close to or merge into the same lane. Finally, the chains of platooning vehicles can safely exit the congestion using scheduled orders. The simulation results demonstrate that the proposed heuristic approach can reduce up to 22% of the delay for the last few vehicles driving through the congestion area in typical traffic density cases with the best platoon size configuration, which is a significant enhancement compared to the existing schemes. Full article
(This article belongs to the Special Issue Vehicular Edge Computing and Networking)
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15 pages, 618 KiB  
Article
Intelligent Task Offloading in Fog Computing Based Vehicular Networks
by Ahmad Naseem Alvi, Muhammad Awais Javed, Mozaherul Hoque Abul Hasanat, Muhammad Badruddin Khan, Abdul Khader Jilani Saudagar, Mohammed Alkhathami and Umar Farooq
Appl. Sci. 2022, 12(9), 4521; https://doi.org/10.3390/app12094521 - 29 Apr 2022
Cited by 11 | Viewed by 2099
Abstract
Connected vehicles in vehicular networks will lead to a smart and autonomous transportation system. These vehicles have a large number of applications that require wireless connectivity by using cellular vehicle-to-everything (C-V2X). The infrastructure of C-V2X comprises multiple roadside units (RSUs) that provide direct [...] Read more.
Connected vehicles in vehicular networks will lead to a smart and autonomous transportation system. These vehicles have a large number of applications that require wireless connectivity by using cellular vehicle-to-everything (C-V2X). The infrastructure of C-V2X comprises multiple roadside units (RSUs) that provide direct connectivity with the on-road vehicles. Vehicular traffic applications are mainly categorized into three major groups such as emergency response traffic, traffic management and infotainment traffic. Vehicles have limited processing capabilities and are unable to process all tasks simultaneously. To process these offloaded tasks in a short time, fog servers are placed near the RSUs. However, it is sometimes not possible for the fog computing server to process all offloaded tasks. In this work, a utility function for the RSU to process these offloaded tasks is designed. In addition, a knapsack-based task scheduling algorithm is proposed to optimally process the offloaded tasks. The results show that the proposed scheme helps fog nodes to optimally scrutinize the high-priority offloaded tasks for task execution resulting in more than 98% of emergency tasks beingprocessed by fog computing nodes. Full article
(This article belongs to the Special Issue Vehicular Edge Computing and Networking)
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