European R&D project – 2022
Autonomous and self-organized artificial intelligent orchestrator for a greener industry 4.0 (TALON)
Project details
Reference
Execution period
01/10/2022 - 30/09/2025
Type
Competitive collaborative R&D
Amount
235.575,00€
Funding body
EUROPEAn COMMISSION
Coordinator
Engineering Ingegneria Informatica (Italy)
Consortium partners
18
Total budget
€3.77M
Consortium and total budget data: CORDIS, European Commission.
CIGIP team
Description
Next-generation industrial systems promise to deliver unprecedented excellence not only in terms of performance, but also explainability, trustworthiness and transparency. To achieve this new objectives, state-of-the-art concepts of artificial intelligence (AI), edge-to-cloud (E2C) computing, blockchain, and visualisation need to be de-risked and applied. Motivated by this, TALON aims at sculpturing the road towards the next Industrial revolution by developing a fully-automated AI architecture capable of bringing intelligence near the edge in a flexible, adaptable, explainable, energy and data efficient manner. TALON architecture consists of three fundamental pillars: a) an AI orchestrator that coordinates the network and service orchestrators in order to optimise the edge vs cloud relationship, while boosting reusability of datasets, algorithms and models by deciding where each one should be placed; b) a lightweight hierarchical blockchain schemes that introduce new service models and applications under a privacy and security umbrella; and c) new digital-twin empowered transfer learning and visualization approaches that enhance AI trustworthiness and transparency. It combines the benefits of AI, edge and cloud networking, as well as blockchain and DTs, optimized by means of a) new key performance indicators that translate the AI benefits into insightful metrics; b) novel theoretical framework for the characterisation of the AI; c) blockchain used to deliver personalised & perpetual protection based on security, privacy and trust mechanisms; d) AI approaches for automatically and co-optimising edge and cloud resources as well as the AI execution nodes; e) semantic AI to reduce the learning latency and enhance reusability; and f) digital twins that visualize the AI outputs and together with human-in-the-loop approaches. All the technological breakthroughs are demonstrated, validated and evaluated by means of proof-of-concept simulation and four real-world pilots.