A paper jointly authored by 5G-STARDUST partner CNIT and by the Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi” of University of Bologna, created for the Journal of Sensor and Actuator Networks has been just released online by MDPI.

“Is it possible to exploit different Distributed Learning algorithms for managing Internet Of Vehicles applications in a Non-Terrestrial Network? In this work, we propose to use Network Slicing as a framework for deploying different distributed learning algorithms for coping with heterogenous services and environments faced by vehicles moving around the globe.” With this words, Daniele Tarchi (University of Bologna), introduces the core themes of the paper “Network Sliced Distributed Learning-as-a-Service for Internet of Vehicles Applications in 6G Non-Terrestrial Network Scenarios”, which has been recently released by MDPI.

The paper, which Tarchi co-authored alongside David Naseh (University of Bologna) and Swapnil S. Shinde (University of Bologna, CNIT) features online as part of the Journal of Sensor and Actuator Networks and was created within the framework of 5G STARDUST and Fondazione RESTART ITA-NTN project. Here below, we offer an excerpt of the publications’ abstract, following-up from Tarchi’s introduction:

In the rapidly evolving landscape of next-generation 6G systems, the integration of AI functions to orchestrate network resources and meet stringent user requirements is a key focus. Distributed Learning (DL), a promising set of techniques that shape the future of 6G communication systems, plays a pivotal role. Vehicular applications, representing various services, are likely to benefit significantly from the advances of 6G technologies, enabling dynamic management infused with inherent intelligence. However, the deployment of various DL methods in traditional vehicular settings with specific demands and resource constraints poses challenges. The emergence of distributed computing and communication resources, such as the edge-cloud continuum and integrated terrestrial and non-terrestrial networks (T/NTN), provides a solution. Efficiently harnessing these resources and simultaneously implementing diverse DL methods becomes crucial, and Network Slicing (NS) emerges as a valuable tool. This study delves into the analysis of DL methods suitable for vehicular environments alongside NS. Subsequently, we present a framework to facilitate DL-as-a-Service (DLaaS) on a distributed networking platform, empowering the proactive deployment of DL algorithms. This approach allows for the effective management of heterogeneous services with varying requirements. The proposed framework is exemplified through a detailed case study in a vehicular integrated T/NTN with diverse service demands from specific regions. Performance analysis highlights the advantages of the DLaaS approach, focusing on flexibility, performance enhancement, added intelligence, and increased user satisfaction in the considered T/NTN vehicular scenario.

If you’re interested in learning more, the full paper is available for download both on MDPI or in the “Scientific publications” section of this website.