RT Journal Article
T1 On Enabling 5G Automotive Systems Using Follow Me edge-Cloud Concept
A1 Aissioui, Abdelkader
A1 Ksentini, Adlen
A1 Gueroui, Abdelhak Mourad
A1 Taleb, Tarik
AB One of the key targets of the upcoming 5G system is to build a mobile network architecture that supports not onlyclassical mobile broadband applications (i.e., Internet and IMS), but also vertical industry services, such as those of automotive systems, e-health, public safety, and smart grid. Vertical industry is known to have specific needs that cannot be sustained by the current cellular networks. More notably, automotive systems require strict quality of service in terms of ultrashort latency for vehicle-toinfrastructure/ network (V2I/N) communications. In this paper, weintroduce the Follow Me edge-Cloud (FMeC) concept, leveraging the mobile edge computing (MEC) architecture to sustain requirements of the 5G automotive systems. Assuming that automotive services are deployed on MEC entities, FMeC ensures low-latency access to these services by guaranteeing that vehicles (i.e., as well as user equipment on board vehicles) always connect to nearest automotive service. Besides the FMeC architecture, our contribution in this paper consists in presenting a projection of the FMeC solution on an automated driving use case that integrates automotive and Telco infrastructures, to realize the vision of future 5G automotive systems.We introduce the envisioned software defined networking/OpenFlow-based architecture and our mobility-aware framework based on a set of building blocks that permit achieving the automated driving requirements within 5G network. The evaluation results, obtained conjointly through theoretical analysis and computer simulation, showthat our proposed solution outperforms baseline approaches in meeting the automated driving latency requirement and minimizing the incurred global cost.
PB IEEE
SN 1939-9359
SN Print 0018-9545
YR 2018
FD 2018-06-18
LK https://hdl.handle.net/10016/27140
UL https://hdl.handle.net/10016/27140
LA eng
NO This work was supported in part by the Academy of Finland Project CSN under Grant 311654 and in part by the European Union’s Horizon 2020 research and innovation program under the 5G!Pagoda project under Grant 723172 and EU H2020 5G-Transformer Project under Grant 761536.
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RD 27 jul. 2024