Deliverable D3.3: Final design of NI solutions and services exploiting zero-trust exposure and compute continuum layers

This document provides a comprehensive overview of final design of all the Network Intelligence (NI) solutions for enhancing mobile network security, efficiency, and reliability developed in ORIGAMI. The deliverable emphasizes the role of machine learning, artificial intelligence, and advanced analytics in building the technology that supports ORIGAMI’s novel architectural components (namely, the Global Service Based Architecture, the Zero-Trust Layer and the Compute Continuum Layer). The project structures this report around three key network domains: the Radio Access Network (RAN), the Transport Network (TN), and the Core Network (CN). The project details the definitive design of the NI solutions across RAN, TN, and CN domains, addressing the specific Barriers the project identified in WP2. RAN solutions include task offloading, xApp conflict mitigation, and Machine Learning (ML) for energy efficiency across 12 use-cases. TN focuses on embedding ML in hardware for scalable user-plane intelligence, with 2 use-cases. CN presents 8 solutions for operational optimisation, security, and flexible operator models.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-06-30
DOI
https://doi.org/10.5281/zenodo.20916609
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Deliverable D3.3: Final design of NI solutions and services exploiting zero-trust exposure and compute continuum layers

Marco Fiore
Zenodo (CERN European Organization for Nuclear Research)
IoT and Edge/Fog Computing
article

Deliverable D3.3: Final design of NI solutions and services exploiting zero-trust exposure and compute continuum layers

Marco Fiore
article en

Abstract

This document provides a comprehensive overview of final design of all the Network Intelligence (NI) solutions for enhancing mobile network security, efficiency, and reliability developed in ORIGAMI. The deliverable emphasizes the role of machine learning, artificial intelligence, and advanced analytics in building the technology that supports ORIGAMI’s novel architectural components (namely, the Global Service Based Architecture, the Zero-Trust Layer and the Compute Continuum Layer). The project structures this report around three key network domains: the Radio Access Network (RAN), the Transport Network (TN), and the Core Network (CN). The project details the definitive design of the NI solutions across RAN, TN, and CN domains, addressing the specific Barriers the project identified in WP2. RAN solutions include task offloading, xApp conflict mitigation, and Machine Learning (ML) for energy efficiency across 12 use-cases. TN focuses on embedding ML in hardware for scalable user-plane intelligence, with 2 use-cases. CN presents 8 solutions for operational optimisation, security, and flexible operator models.

Zenodo (CERN European Organization for Nuclear Research)
European Commission
Affordable and clean energy
Openalex Percentile: Top 6%
IoT and Edge/Fog Computing
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Deliverable D3.3: Final design of NI solutions and services exploiting zero-trust exposure and compute continuum layers — Marco Fiore · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS