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The 5G-IANA platform: Bringing far-edge resources and ML potential to the disposal of automotive third parties

Authors: Francesca Moscatelli; Thanos Xirofotos; Amr Rizk; Nehal Baganal-Krishna; Edoardo Bonetto; Eirini Liotou; Angelos Amditis;

The 5G-IANA platform: Bringing far-edge resources and ML potential to the disposal of automotive third parties

Abstract

The advent of 5G has been a game-changer in the Automotive Vertical sector. 5G acts as an enabler of advanced networking architectures which, in turn, allow the development of novel services, by attracting and engaging third parties to experiment using available 5G infrastructure and connectivity. 5GIANA is a Horizon 2020 ICT-41 project that targets to create such an advanced open experimentation platform, bringing powerful novelty to third parties through the integration and provisioning of far-edge resources for network orchestration purposes, and through the offering of Machine Learning knowledge as an addon service to them. The scope of this paper is, therefore, to present the 5G-IANA project's overall goals, by emphasizing the most important expected innovations from the project regarding far-edge resources’ orchestration and ML as a service provisioning.

Keywords

Informatik, 5G, automotive vertical, edge resources, orchestration, distributed machine learning

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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