Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
Exploring Emerging Cloud-Based Technologies and Future Directions...
share announcement

Exploring Emerging Cloud-Based Technologies and Future Directions in VANETs

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

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 4264

Special Issue Editor

Prof. Dr. Yolanda Blanco Fernández

E-Mail
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,

The deployment of 5G networks, the evolution of concepts such as the Internet of Things (IoT) and smart cities, along with the increasing availability of autonomous vehicles promote the development of next-generation VANETs. These wireless networks face challenging issues related to high mobility, dynamic network topologies, intermittent connectivity, or support for a wide heterogeneity of applications with demanding communication and computational requirements (low latencies, high reliability, location-awareness, seamless connectivity, handling of massive amounts of network traffic, etc.). In recent years, some researchers have taken advantage of cloud computing (CC) to extend the capabilities of these wireless networks in a dynamic vehicular environment. However, the exchange of information with the conventional cloud jeopardizes some critical requisites of VANETs, especially those bound to supporting high mobility and low latencies. To fight this problem, a good number of research works exploit different edge cloud computing (ECC) approaches in resolving challenges of future VANETs, ranging from fog computing to mobile edge computing (MEC) and cloudlets. More recently, the usage of software-defined networking (SDN) with fog computing has gained momentum. On the one hand, fog computing provides very low latencies (bringing the cloud to the edge of the VANET) and supports vehicles with high mobility. On the other hand, SDN supplies flexibility and scalability to the VANET, separating the data plane (used for forwarding) and the control plane (used for network control traffic).

This Special Issue aims to present papers that address problems that still remain open in the usage of emerging cloud-based technologies in future VANETs, exploring aspects such as deployment, architectures, routing, security-related issues, etc. The topics covered include but not are limited to applications (safety, comfort, infotainment), vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication environments, routing protocols, software-defined vehicular network (SDVN) architectures, security and privacy related solutions (availability, confidentiality, authentication, data integrity), 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

  • VANETs
  • Software-defined networking (SDN)
  • Edge cloud computing (ECC)
  • Fog computing
  • Mobile edge computing (MEC)
  • Cloudlets
  • Cloud-based mobile augmentation (CMA)
  • Software-defined vehicular networks (SDVN)

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 898 KiB  
Article
RSU-Aided Optimal Member Replacement Scheme with Improved Mobility Prediction for Vehicular Clouds in VANETs
by Youngju Nam, Hyunseok Choi, Yongje Shin, Dick Mugerwa and Euisin Lee
Electronics 2022, 11(17), 2787; https://doi.org/10.3390/electronics11172787 - 04 Sep 2022
Viewed by 1070
Abstract
The technique of vehicular clouds is considered an attractive approach in VANETs, because it provides a requester vehicle the ability to use resources of neighborhood vehicles (called cloud member vehicles) to construct a vehicular cloud to use next-generation vehicular applications during driving. Generally, [...] Read more.
The technique of vehicular clouds is considered an attractive approach in VANETs, because it provides a requester vehicle the ability to use resources of neighborhood vehicles (called cloud member vehicles) to construct a vehicular cloud to use next-generation vehicular applications during driving. Generally, member vehicles can move along different routes from the route of the requester vehicle in intersections and, as a result, leave the vehicular cloud. Then, the leaving member vehicle should be replaced by new member vehicles at intersections to reconstruct the vehicular cloud. However, identifying optimal replacement vehicles among many vehicles at intersections is a very difficult task involving minimizing the waste of resources of vehicles due to their irregular mobility. Thus, we propose an optimal member replacement scheme that finds optimal replacement vehicles through the improved mobility prediction of vehicles by borrowing the computational ability of RSUs on intersections. The proposed scheme first makes an improved mobility prediction model by combining both the trajectory prediction of vehicles using the Markov model and the location prediction of vehicles using the Gaussian distribution. Through the improved mobility prediction model, the proposed scheme then determines the leaving member vehicles and calculates their own leaving time. Next, the proposed scheme addresses the problem to find optimal replacement vehicles to minimize the waste resource and solves it through an integer linear programming. For the performance evaluation of the proposed scheme, we implement it in an NS-3 simulator, which includes the Manhattan mobility model, to reflect the mobility of vehicles on roads. Simulation results conducted in various environments verify that the proposed scheme achieves better performance than the existing scheme. Full article
(This article belongs to the Special Issue Exploring Emerging Cloud-Based Technologies and Future Directions in VANETs)
►▼ Show Figures

Figure 1

19 pages, 1153 KiB  
Article
Deep Reinforcement Learning Based Resource Allocation with Radio Remote Head Grouping and Vehicle Clustering in 5G Vehicular Networks
by Hyebin Park and Yujin Lim
Electronics 2021, 10(23), 3015; https://doi.org/10.3390/electronics10233015 - 02 Dec 2021
Cited by 9 | Viewed by 2311
Abstract
With increasing data traffic requirements in vehicular networks, vehicle-to-everything (V2X) communication has become imperative in improving road safety to guarantee reliable and low latency services. However, V2X communication is highly affected by interference when changing channel states in a high mobility environment in [...] Read more.
With increasing data traffic requirements in vehicular networks, vehicle-to-everything (V2X) communication has become imperative in improving road safety to guarantee reliable and low latency services. However, V2X communication is highly affected by interference when changing channel states in a high mobility environment in vehicular networks. For optimal interference management in high mobility environments, it is necessary to apply deep reinforcement learning (DRL) to allocate communication resources. In addition, to improve system capacity and reduce system energy consumption from the traffic overheads of periodic messages, a vehicle clustering technique is required. In this paper, a DRL based resource allocation method is proposed with remote radio head grouping and vehicle clustering to maximize system energy efficiency while considering quality of service and reliability. The proposed algorithm is compared with three existing algorithms in terms of performance through simulations, in each case outperforming the existing algorithms in terms of average signal to interference noise ratio, achievable data rate, and system energy efficiency. Full article
(This article belongs to the Special Issue Exploring Emerging Cloud-Based Technologies and Future Directions in VANETs)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop