Cargando…

Implementation of MEC-Assisted Collective Perception in an Integrated Artery/Simu5G Simulation Framework

Advanced vehicle-to-everything (V2X) safety applications must operate with ultra-low latency and be highly reliable. Therefore, they require sophisticated supporting technologies. This is especially true for cooperative applications, such as Collective Perception (CP), where a large amount of data c...

Descripción completa

Detalles Bibliográficos
Autores principales: Kovács, Gergely Attila, Bokor, László
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10534514/
https://www.ncbi.nlm.nih.gov/pubmed/37766023
http://dx.doi.org/10.3390/s23187968
_version_ 1785112412409561088
author Kovács, Gergely Attila
Bokor, László
author_facet Kovács, Gergely Attila
Bokor, László
author_sort Kovács, Gergely Attila
collection PubMed
description Advanced vehicle-to-everything (V2X) safety applications must operate with ultra-low latency and be highly reliable. Therefore, they require sophisticated supporting technologies. This is especially true for cooperative applications, such as Collective Perception (CP), where a large amount of data constantly flows among vehicles and between vehicles and a network intelligence server. Both low and high-level support is needed for such an operation, meaning that various access technologies and other architectural elements also need to incorporate features enabling the effective use of V2X applications with strict requirements. The new 5G core architecture promises even more supporting technologies, like Multi-access Edge Computing (MEC). To test the performance of these technologies, an integrated framework for V2X simulations with 5G network elements is proposed in the form of combining Simu5G, a standalone 5G implementation, with the go-to V2X-simulator, Artery. As a first step toward a fully functional MEC-assisted CP Service, an extension to Simu5G’s edge implementation is introduced. The edge application is responsible for dispatching the Collective Perception Messages generated by the vehicles via the 5G connectivity so that a MEC server provided by the network can process incoming data. Simulation results prove the operability of the proposed integrated system and edge computing’s potential in assisting V2X scenarios.
format Online
Article
Text
id pubmed-10534514
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-105345142023-09-29 Implementation of MEC-Assisted Collective Perception in an Integrated Artery/Simu5G Simulation Framework Kovács, Gergely Attila Bokor, László Sensors (Basel) Article Advanced vehicle-to-everything (V2X) safety applications must operate with ultra-low latency and be highly reliable. Therefore, they require sophisticated supporting technologies. This is especially true for cooperative applications, such as Collective Perception (CP), where a large amount of data constantly flows among vehicles and between vehicles and a network intelligence server. Both low and high-level support is needed for such an operation, meaning that various access technologies and other architectural elements also need to incorporate features enabling the effective use of V2X applications with strict requirements. The new 5G core architecture promises even more supporting technologies, like Multi-access Edge Computing (MEC). To test the performance of these technologies, an integrated framework for V2X simulations with 5G network elements is proposed in the form of combining Simu5G, a standalone 5G implementation, with the go-to V2X-simulator, Artery. As a first step toward a fully functional MEC-assisted CP Service, an extension to Simu5G’s edge implementation is introduced. The edge application is responsible for dispatching the Collective Perception Messages generated by the vehicles via the 5G connectivity so that a MEC server provided by the network can process incoming data. Simulation results prove the operability of the proposed integrated system and edge computing’s potential in assisting V2X scenarios. MDPI 2023-09-19 /pmc/articles/PMC10534514/ /pubmed/37766023 http://dx.doi.org/10.3390/s23187968 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kovács, Gergely Attila
Bokor, László
Implementation of MEC-Assisted Collective Perception in an Integrated Artery/Simu5G Simulation Framework
title Implementation of MEC-Assisted Collective Perception in an Integrated Artery/Simu5G Simulation Framework
title_full Implementation of MEC-Assisted Collective Perception in an Integrated Artery/Simu5G Simulation Framework
title_fullStr Implementation of MEC-Assisted Collective Perception in an Integrated Artery/Simu5G Simulation Framework
title_full_unstemmed Implementation of MEC-Assisted Collective Perception in an Integrated Artery/Simu5G Simulation Framework
title_short Implementation of MEC-Assisted Collective Perception in an Integrated Artery/Simu5G Simulation Framework
title_sort implementation of mec-assisted collective perception in an integrated artery/simu5g simulation framework
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10534514/
https://www.ncbi.nlm.nih.gov/pubmed/37766023
http://dx.doi.org/10.3390/s23187968
work_keys_str_mv AT kovacsgergelyattila implementationofmecassistedcollectiveperceptioninanintegratedarterysimu5gsimulationframework
AT bokorlaszlo implementationofmecassistedcollectiveperceptioninanintegratedarterysimu5gsimulationframework