Imagine a brain for a factory that automatically decides whether to process data on the spot or send it to a distant cloud to save energy and time. It uses a digital ledger like a secure diary to keep all transactions private and trustworthy. It also creates a virtual twin of the system so humans can see exactly why the AI is making certain decisions.
Industrial AI often suffers from high latency, excessive energy consumption, and a 'black box' nature that makes it hard for humans to trust. Additionally, securing data across distributed edge networks is complex and resource-heavy.
A fully-automated AI platform featuring an E2C orchestrator for resource optimization, a lightweight hierarchical blockchain for security, and digital twins for AI visualization.
If you are an automated assembly plant dealing with high latency in AI decision-making — this project developed an E2C orchestrator that brings intelligence closer to the edge to improve speed and energy efficiency. It ensures the system can self-heal and recover from errors automatically.
If you are a warehouse automation provider dealing with data privacy and security risks across distributed nodes — this project developed a lightweight hierarchical blockchain toolbox. This provides a security umbrella for service models while maintaining high performance.
If you are an industrial grid operator dealing with the high energy cost of running AI models — this project developed a Greener-AI orchestrator that optimizes where datasets and algorithms are placed. This reduces the carbon footprint of industrial AI operations.
Based on available project data, no specific pricing or cost structures are mentioned as this was a research and innovation action.
Yes, the technology was validated through 4 real-world pilots and proof-of-concept simulations to ensure it works in actual industrial settings.
Based on available project data, specific licensing terms are not listed, but the consortium includes 11 industry partners and 8 SMEs who likely share the IP.
It uses an Edge-to-Cloud (E2C) orchestrator that coordinates network and service layers to optimize the relationship between local edge nodes and the cloud.
The project included a specific phase for regulation and standardization to ensure the AI is explainable, trustworthy, and transparent.
The consortium is heavily industry-driven, with 11 companies representing 61% of the 18 partners. The presence of 8 SMEs suggests a strong focus on commercial agility and practical application, while 5 universities and 2 research centers provided the theoretical basis for the AI and blockchain components.
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