Imagine if the internet's traffic lights could talk to each other across different cities and companies to prevent jams before they happen. This work creates a smart brain that manages both wireless and fiber-optic cables together. It ensures that data moves smoothly and securely without needing one single giant boss in charge.
Current network controls are fragmented between wireless and fiber-optic systems and cannot easily handle multi-vendor environments. This leads to inefficiencies in energy use and vulnerabilities to data security threats.
A modular software AI Controller (AIC) that coordinates radio and optical networks. It includes modules for data integrity, privacy-preserving governance, and energy-efficiency evaluation.
If you are a port operator dealing with lagging automated cranes and vehicle coordination — this project developed a decentralized AI control system that coordinates radio and optical networks. This ensures faster and more reliable performance for critical automation tasks.
If you are a factory integrator dealing with unstable connectivity between sensors and controllers — this project developed an AI Controller that unifies different network domains. This allows for self-repairing networks that reduce downtime in production lines.
If you are a provider dealing with high energy costs and complex multi-vendor hardware — this project developed lightweight AI algorithms and energy-efficiency models. This helps balance power use against speed and accuracy.
Based on available project data, no specific pricing or cost structures are provided as this is a research and innovation action.
The project is designed for large-scale application by integrating with EU and US testbeds and working with global standardization groups.
Based on available project data, specific licensing terms are not mentioned, though the project focuses on global validation and standardization.
The project focuses on security and trust, specifically addressing data poisoning and privacy breaches to meet governance standards.
The system uses a modular software AI Controller designed to work across different vendors and network domains, including radio and optical layers.
The consortium is well-balanced for technology transfer, featuring 10 partners across 6 countries. With a 40% industry ratio (4 companies, including 1 SME), the project ensures that the academic research from 4 universities and 2 research centers is grounded in commercial reality and industrial requirements.
Contact the Board of the College of the Holy & Undivided Trinity of Queen Elizabeth near Dublin
Contact us to explore licensing opportunities for the AI Controller modules.
