Enabling Far-Edge Intelligent Services with Network Applications: The Automotive Case

Abstract

The fifth generation of mobile networks (5G) is rapidly reaching deployment across the globe, promising a series of advances for vertical service providers, both in terms of performance and in terms of operational capabilities. In this context, the 5G-IANA Network Application platform focuses on the rapidly advancing domain of intelligent, data centric, Artificial Intelligence/Machine Learning (AI/ML)-enabled applications, with a particular focus on the automotive domain. In this article, we present the key functional features designed for the support of such services including the integration of (mobile) far-edge resources, as well as ML-aware orchestration primitives. This includes novel features such as decision support for the optimal distribution of end-to-end ML pipelines, as well as run-time support for client selection in Federated Learning setups, far-edge failure handling and distribution drift aware lifecycle management. Such features come to address a series of limitations associated with legacy 5G management & orchestration systems, such as resource consumption of data centric services and privacy support. In this context, we further discuss the new opportunities arising for service provisioning and corresponding business models in the automotive ecosystem, with a particular emphasis on the implications of the emerging data and/or ML-model sharing schemes.