MUNSTER TECHNOLOGICAL UNIVERSITY

TELEMETRY will provide trustworthy tools that enable the continuous assessment of heterogenous, interlinked components & systems that constitute IoT ecosystems (interconnected IoT devices with hardware, software, services and communications infrastructure). Addressing all aspects of their lifecycle, the TELEMETRY holistic methodology and toolkit incorporates: testing for component development, testing & monitoring for component integration into systems, testing & monitoring for operation of systems. TELEMETRY will deliver advances in cybersecurity testing and runtime monitoring through the use of novel machine learning models and algorithms for real-time anomaly detection; dynamic risk assessment to simulate likelihood and severity of threat consequences; reputation management and privacy-preserving data sharing across independent entities (e.g. supply chains), IoT device emulation and analysis environment and lightweight approaches for trusted updates; all of which that promotes a cycle of continuous improvement and assurance across design and runtime phases. TELEMETRY will leverage 3 exemplar use cases representing diverse, complex IoT ecosystems and IoT supply chains in aerospace, smart manufacturing and telecommunications domains to drive the design and validation of the proposed tools and methodologies. This will lead to significant improvements with respect to accuracy of threat and vulnerability detection, response time and cost of testing and verification of IoT ecosystems. TELEMETRY will promote open source and knowledge sharing through engagement with relevant communities throughout the project for consultation, dissemination and exploitation of its results.

EC contribution:

EUR 516k

Start:

2023-09-01

End:

2026-11-30

Status:

SIGNED

Scheme:

HORIZON-RIA

Call:

HORIZON-CL3-2022-CS-01

View on CORDIS (DOI 10.3030/101119747)

The evolution of 6G wireless networks relies on advanced multiple-access technologies that improve efficiency and scalability. Reconfigurable Intelligent Surfaces (RIS) are set to transform 6G by enabling dynamic control of signal propagation channels. This project focuses on optimizing User Equipment (UE) pairings with Resource Blocks (RBs) and RISs in RIS-assisted Non-Orthogonal Multiple Access (NOMA) environments, exploring various configurations to maximize system throughput, fairness, and energy efficiency. By integrating active and passive beamforming, our approach not only enhances communication performance but also reduces the carbon footprint by minimizing the need for additional base stations through efficient frequency reuse. With Munster Technological University (MTU) affiliated with ADAPT Centre, and secondment at Dell Technologies, this research is strategically positioned for high-impact outcomes. The project introduces a novel technique for intelligent role switching in low mobility networks (LMNs), enabling dynamic shifts between near and far users to enhance connectivity time. The methodology incorporates optimization algorithms such as Simulated Annealing, Hill Climbing, Genetic Algorithms, and Random Walk for heuristic solutions. For LMNs, we will develop and evaluate models based on stochastic geometry and deep reinforcement learning (DRL) (asynchronous advantage actor-critic) to optimize the angle of reflection (AoR) for RIS elements. Four-month secondment at Dell, Ireland, is a key aspect where industry-grade validation will assess the performance of RO3 and refine the interplay between the edge-cloud continuum for the DRL. These models aim to address challenges in RIS-assisted networks, supporting seamless 6G connectivity in smart and sustainable cities. Ultimately, the research will advance green communications by optimizing network designs for energy-efficient infrastructure, aligning with EU goals for sustainable smarter cities.

EC contribution:

EUR 269k

Start:

2027-01-04

End:

2029-01-03

Status:

SIGNED

Scheme:

HORIZON-TMA-MSCA-PF-EF

Call:

HORIZON-MSCA-2025-PF

View on CORDIS (DOI 10.3030/101273667)

GenDAI will leverage metagenomic potential to deliver powerful diagnostic results facilitating the development of Personalized Medicine, addressing assay research and development as well as productive clinical diagnostics in a comprehensive way by creating a highly innovative medical diagnostics platform that supports microbiome profiling with novel biomarkers using Artificial Intelligence (AI). This innovative platform will allow clinicians to assess and monitor patients while complying with strict regulatory requirements of laboratory diagnostics. Ultimately, GenDAI will contribute to accelerate conversion of innovative ideas and technology solutions into breakthroughs in medical analyses services. Delivery of GenDAI tangible outputs will be driven by the following Research and Innovation Objectives: a) Create new metagenomic datasets containing microbiome samples from stool of patients under informed consent suffering from inflammatory bowel disease (IBD); b) Develop and integrate the GenDAI Diagnostics Workflow, focusing on implementing a fully automated data processing pipeline; c);Provide a robust, cloud-based foundation for the development of the platform that integrates advanced data management and knowledge infrastructure focusing on security, reproducibility and long-term archiving (GenDAI Safe); d) Develop and improve AI methods to identify relevant biomarkers and classify corresponding metagenomic sequences in order to characterise microbiome profiles to provide a personalised diagnostic result of patients’ state of health (GenDAI Discovery); e) Deliver innovative visual user interfaces and interactive clinical reporting (GenDAI Interactive Reporting) and f) Deliver the marketable, regulatory compliant GenDAI technology and tool suite.

EC contribution:

EUR 221k

Start:

2024-09-01

End:

2027-08-31

Status:

SIGNED

Scheme:

HORIZON-TMA-MSCA-SE

Call:

HORIZON-MSCA-2023-SE-01

View on CORDIS (DOI 10.3030/101182801)