Dynamic Mobile Network Slicing Through Vehicular Traffic Analysis
Network slicing has emerged as a transformative enabler in 5G networks, offering tailored communication services for diverse traffic types on shared network infrastructure. In the context of autonomous driving and smart mobility, the ability to dynamically prioritize and manage sensor dataȁ...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/10988659/ |
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| _version_ | 1850217499869577216 |
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| author | Alvaro Gabilondo Zaloa Fernandez Angel Martin Mikel Zorrilla Pablo Angueira Jon montalban |
| author_facet | Alvaro Gabilondo Zaloa Fernandez Angel Martin Mikel Zorrilla Pablo Angueira Jon montalban |
| author_sort | Alvaro Gabilondo |
| collection | DOAJ |
| description | Network slicing has emerged as a transformative enabler in 5G networks, offering tailored communication services for diverse traffic types on shared network infrastructure. In the context of autonomous driving and smart mobility, the ability to dynamically prioritize and manage sensor data—ranging from high-bandwidth video streams to low-latency text and binary position and coordination messages—plays a pivotal role in ensuring safe and efficient operation. This paper proposes a dynamic mobile network slicing framework designed to analyse vehicular traffic and adapt slicing policies to optimize resource allocation for autonomous driving applications. By leveraging distributed and disaggregated 5G network architectures, the proposed solution ensures seamless propagation of slicing policies across radio access networks (RAN) and core systems building end-to-end network slices. Experimental evaluations in scenarios such as Automated Guided Vehicle (AGV)-assisted operations in industrial environments demonstrate significant performance improvements, including a reduction in packet loss from 65% to 0% under congested network conditions. The results highlight the potential of dynamic slicing to enhance communication reliability and performance in autonomous driving ecosystems, supporting the seamless exchange of diverse sensor data types. |
| format | Article |
| id | doaj-art-972fa516dc3b4e90a55d8de2ebfe0c4c |
| institution | OA Journals |
| issn | 2644-1330 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-972fa516dc3b4e90a55d8de2ebfe0c4c2025-08-20T02:08:02ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302025-01-0161464148010.1109/OJVT.2025.356711610988659Dynamic Mobile Network Slicing Through Vehicular Traffic AnalysisAlvaro Gabilondo0https://orcid.org/0000-0003-3576-9058Zaloa Fernandez1https://orcid.org/0000-0002-2201-4732Angel Martin2https://orcid.org/0000-0002-1213-6787Mikel Zorrilla3https://orcid.org/0000-0003-2589-2490Pablo Angueira4https://orcid.org/0000-0002-5188-8412Jon montalban5https://orcid.org/0000-0003-0309-3401Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), San Sebastián, SpainVicomtech Foundation, Basque Research and Technology Alliance (BRTA), San Sebastián, SpainVicomtech Foundation, Basque Research and Technology Alliance (BRTA), San Sebastián, SpainVicomtech Foundation, Basque Research and Technology Alliance (BRTA), San Sebastián, SpainDepartment of Communications Engineering, University of the Basque Country (UPV/EHU), Bilbao, SpainDepartment of Communications Engineering, University of the Basque Country (UPV/EHU), Bilbao, SpainNetwork slicing has emerged as a transformative enabler in 5G networks, offering tailored communication services for diverse traffic types on shared network infrastructure. In the context of autonomous driving and smart mobility, the ability to dynamically prioritize and manage sensor data—ranging from high-bandwidth video streams to low-latency text and binary position and coordination messages—plays a pivotal role in ensuring safe and efficient operation. This paper proposes a dynamic mobile network slicing framework designed to analyse vehicular traffic and adapt slicing policies to optimize resource allocation for autonomous driving applications. By leveraging distributed and disaggregated 5G network architectures, the proposed solution ensures seamless propagation of slicing policies across radio access networks (RAN) and core systems building end-to-end network slices. Experimental evaluations in scenarios such as Automated Guided Vehicle (AGV)-assisted operations in industrial environments demonstrate significant performance improvements, including a reduction in packet loss from 65% to 0% under congested network conditions. The results highlight the potential of dynamic slicing to enhance communication reliability and performance in autonomous driving ecosystems, supporting the seamless exchange of diverse sensor data types.https://ieeexplore.ieee.org/document/10988659/5Gautonomous drivingcore slicingdynamic networknetwork slicingRAN slicing |
| spellingShingle | Alvaro Gabilondo Zaloa Fernandez Angel Martin Mikel Zorrilla Pablo Angueira Jon montalban Dynamic Mobile Network Slicing Through Vehicular Traffic Analysis IEEE Open Journal of Vehicular Technology 5G autonomous driving core slicing dynamic network network slicing RAN slicing |
| title | Dynamic Mobile Network Slicing Through Vehicular Traffic Analysis |
| title_full | Dynamic Mobile Network Slicing Through Vehicular Traffic Analysis |
| title_fullStr | Dynamic Mobile Network Slicing Through Vehicular Traffic Analysis |
| title_full_unstemmed | Dynamic Mobile Network Slicing Through Vehicular Traffic Analysis |
| title_short | Dynamic Mobile Network Slicing Through Vehicular Traffic Analysis |
| title_sort | dynamic mobile network slicing through vehicular traffic analysis |
| topic | 5G autonomous driving core slicing dynamic network network slicing RAN slicing |
| url | https://ieeexplore.ieee.org/document/10988659/ |
| work_keys_str_mv | AT alvarogabilondo dynamicmobilenetworkslicingthroughvehiculartrafficanalysis AT zaloafernandez dynamicmobilenetworkslicingthroughvehiculartrafficanalysis AT angelmartin dynamicmobilenetworkslicingthroughvehiculartrafficanalysis AT mikelzorrilla dynamicmobilenetworkslicingthroughvehiculartrafficanalysis AT pabloangueira dynamicmobilenetworkslicingthroughvehiculartrafficanalysis AT jonmontalban dynamicmobilenetworkslicingthroughvehiculartrafficanalysis |