Research Miguel Camelo Botero

Abstract

This internal UAntwerp project to explore the fundamental challenges in making next-generation networks (NGNs) more intelligent and inherently secure. The core vision is to achieve the deep integration of zero-touch network and service management (ZSM) automation with zero-trust (ZT) security in complex, open, and disaggregated infrastructures, such as the Open Radio Access Network (O-RAN) and Advanced 5G Core. The project posits that NGNs must embed ZT and ZSM as foundational principles, rather than treating security as an afterthought. A major scientific knowledge gap currently exists because research often treats automation (ZSM) and security (ZT) in isolation. We intend to realize the underexplored, intertwined symbiosis between these paradigms: ZSM automation can mitigate the operational complexity of ZT adoption, while ZT principles can safeguard the vulnerabilities inherent in ZSM provisioning. The project aims to achieve four main scientific objectives: 1. Pervasive and Sustainable Network Intelligence: Design and implement sustainable network intelligence via AI/ML-based agents integrated into an intent-based orchestration framework, enabling ZSM capabilities. Key metrics include reducing AI model energy consumption by up to 70% and achieving seamless service recovery under disruptions. 2. Advanced Security and Trust Mechanisms: Develop and embed advanced ZT security mechanisms, including a Public Key Infrastructure (PKI), authentication, authorization, data encryption, and attestation mechanisms. This aims to address O-RAN security gaps, ensuring secure, resilient orchestration with minimal performance degradation (not higher than 10%). The framework will enforce adaptive and context-aware security policies and ensure continuous integrity verification of components. 3. Integration and Validation: Validate the integrated ZSM and ZT framework on real-world testbeds by assessing automation, security robustness, and resilience. Validation focuses on achieving zero or near-zero service disruption and high reliability in critical use cases. The project brings together expertise and advanced testbeds to deliver a robust framework that aims not only to advance the scientific state-of-the-art but also to lay the groundwork for the future industrial uptake of intelligent, trustworthy, and cost-efficient NGNs.

Researcher(s)

• Promoter: Marquez-Barja Johann
• Co-promoter: Camelo Botero Miguel

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The overarching objective of 6G-TWIN is to provide the foundation for the design, implementation and validation of an AI-native reference architecture for 6G systems that incorporates Network Digital Twins (NDT) as a core mechanism for the end-to-end, realtime optimisation, management and control of highly dynamic and complex network scenarios. To achieve this objective, 6G-TWIN will deliver methods, modelling and simulation solutions for the definition, creation and management of multi-layered virtual representations of future 6G systems, where heterogeneous domains (i.e., edge, fog and cloud) and communication technologies (e.g., cellular, optical and Non-Terrestrial Networks (NTN)) coexist. The project solutions will be demonstrated in two complementary use cases addressing mobility and energy-efficiency challenges, aligned with the expected use cases of 6G and the Key Performance Indicators (KPI) defined in previously funded projects (including SNS JU STREAM-C/D-2022). Finally, the participation of Small and Medium-sized Enterprises (SMEs) will ensure that the 6G-TWIN consortium pays particular attention to the replication, reengineering and exploitation of the project outcomes, regularly aligning the requirements of standardisation bodies with predicted market needs.

Researcher(s)

• Promoter: Famaey Jeroen
• Co-promoter: Camelo Botero Miguel

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

DAEMON - The success of Beyond 5G (B5G) systems will largely depend on the quality of the Network Intelligence (NI) that will fully automate network management. Artificial Intelligence (AI) models are commonly regarded as the cornerstone for NI design; indeed, AI models have proven extremely successful at solving hard problems that require inferring complex relationships from entangled and massive (e.g., traffic) data. However, AI is not the best solution for every NI task; and, when it is, the dominating trend of plugging 'vanilla' AI into network controllers and orchestrators is not a sensible choice. Departing from the current hype around AI, DAEMON will set forth a pragmatic approach to NI design. The project will carry out a systematic analysis of which NI tasks are appropriately solved with AI models, providing a solid set of guidelines for the use of machine learning in network functions. For those problems where AI is a suitable tool, DAEMON will design tailored AI models that respond to the specific needs of network functions, taking advantage of the most recent advances in machine learning. Building on these models, DAEMON will design an end-to-end NInative architecture for B5G that fully coordinates NI-assisted functionalities. The advances to NI devised by DAEMON will be applied in practical network settings to: (i) deliver extremely high performance while making an efficient use of the underlying radio and computational resources; (ii) reduce the energy footprint of mobile networks; and (iii) provide extremely high reliability beyond that of 5G systems. To achieve this, DAEMON will design practical algorithms for eight concrete NI-assisted functionalities, carefully selected to achieve the objectives above. The performance of the DAEMON algorithms will be evaluated in real-world conditions via four experimental sites, and at scale with data-driven approaches based on two nationwide traffic measurement datasets, against nine ambitious yet feasible KPI targets.

Researcher(s)

• Promoter: Marquez-Barja Johann
• Co-promoter: Camelo Botero Miguel
• Co-promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project