Periodic Reporting for period 1 - HYPER-AI (Hyper-Distributed Artificial Intelligence Platform for Network Resources Automation and Management Towards More Efficient Data Processing Applications)
Okres sprawozdawczy: 2024-04-01 do 2025-09-30
Objective O1: Design and implement the HYPER-AI open architecture to let swarms of nodes collaboratively build collective intelligence at the edge using decentralized AI/ML.
HOW: Deliver a scalable, self-contained distributed mesh of autonomous abstractions (“Smart-Nodes”). Enable dynamic deployment across the cloud-edge continuum using real-time DLT-based findability/access, and multi-objective optimization of resources (compute, storage, connectivity cost, energy) against diverse performance indices.
Objective O2: Enable dynamic, cognitively informed, optimized decisions across the full application lifecycle (design, execution, maintenance) via continuous inference.
HOW: Let registered entities (devices, nodes, Smart-Nodes, swarms) interpret context and internal state to optimize QoS/QoE. Support continuous incremental learning and a live library of reusable analytic functions for future transfer learning. Provide state estimation for (a) self-awareness during operation/orchestration and (b) fast response, offloading bandwidth-heavy tasks to edge resources while keeping compute-intensive work near cloud. Keep resource use transparent to apps. For critical events, reusable AI enables early detection, recognition, warning, and rapid reaction.
Objective O3: Develop autonomic entities for autonomous Smart-Nodes and rapid coordination of Smart-Node swarms during design and runtime.
HOW: Provide optimization/management tools across network hierarchies and ontology instantiations. Create self-organizing Smart-Nodes that pool available resources from real-time, data-driven situational awareness. Enable dynamic swarming into Meta-Smart-Nodes via decentralized coordination that adapts to emergent behavior and supports collaborative cross-node schemes with shared, otherwise idle meta-resources.
Objective O4: Ensure a secure, private, and robust edge-to-cloud ecosystem.
HOW: Deliver a distributed security & privacy framework using end-to-end encryption, key management, and AI-based intrusion detection. Protect data in use and at rest, preserve privacy for individuals/communities, detect attacks and errors, and enforce consistent, persistent policies via distributed ledger technologies.
Objective O5: Safeguard openness to guarantee interoperability, accessibility, and international cooperation.
HOW: Establish a framework for social innovation with active co-creation and community engagement to maximize adoption. Through Eclipse Foundation Europe, adopt open-science practices and build channels to open-source communities. Run five pilot use cases (Manufacturing, Automotive, Agriculture, Energy, Healthcare) in five countries (one led by a Korean partner). Involve innovative SMEs aligned with consortium skills. Provide open-source snippets and core functions to accelerate external development. Align with EU Open Research principles to stimulate a vibrant, international HYPER-AI community and uptake.
Objective O6: Demonstrate HYPER-AI’s usability, performance, energy efficiency, interoperability, and impact.
HOW: Assess and showcase tools, modules, and the back-end optimization stack across five use cases at multiple pilot sites and scales. Monitor all scenarios under a rigorous evaluation framework. Focus on verifying interoperability in challenging operational environments (Manufacturing, Agriculture, Automotive, Energy, Healthcare).
Objective O7: Drive wide communication, efficient dissemination, and vertical exploitation of results.
HOW: Propose novel, disruptive business models arising from HYPER-AI, analyze market uptake, and offer revised “business-as-usual” models for diverse stakeholders (cloud vendors, policy-makers, telecoms/ISPs, developers, end-users) across the continuum. Explore incentivized, secure sharing of edge resources (including data) to grow Europe’s data economy, support EU single-market data spaces, and foster a trustworthy AI ecosystem. Evaluate Robot-as-a-Service (RaaS)—subscription-based access to robots with updates/maintenance within hybrid cloud-edge settings—and cloud robotics as potential models for the hybrid adaptive and cognitive computing continuum.
HOW: Deliver a scalable, self-contained distributed mesh of autonomous abstractions (“Smart-Nodes”). Enable dynamic deployment across the cloud-edge continuum using real-time DLT-based findability/access, and multi-objective optimization of resources (compute, storage, connectivity cost, energy) against diverse performance indices.
Objective O2: Enable dynamic, cognitively informed, optimized decisions across the full application lifecycle (design, execution, maintenance) via continuous inference.
HOW: Let registered entities (devices, nodes, Smart-Nodes, swarms) interpret context and internal state to optimize QoS/QoE. Support continuous incremental learning and a live library of reusable analytic functions for future transfer learning. Provide state estimation for (a) self-awareness during operation/orchestration and (b) fast response, offloading bandwidth-heavy tasks to edge resources while keeping compute-intensive work near cloud. Keep resource use transparent to apps. For critical events, reusable AI enables early detection, recognition, warning, and rapid reaction.
Objective O3: Develop autonomic entities for autonomous Smart-Nodes and rapid coordination of Smart-Node swarms during design and runtime.
HOW: Provide optimization/management tools across network hierarchies and ontology instantiations. Create self-organizing Smart-Nodes that pool available resources from real-time, data-driven situational awareness. Enable dynamic swarming into Meta-Smart-Nodes via decentralized coordination that adapts to emergent behavior and supports collaborative cross-node schemes with shared, otherwise idle meta-resources.
Objective O4: Ensure a secure, private, and robust edge-to-cloud ecosystem.
HOW: Deliver a distributed security & privacy framework using end-to-end encryption, key management, and AI-based intrusion detection. Protect data in use and at rest, preserve privacy for individuals/communities, detect attacks and errors, and enforce consistent, persistent policies via distributed ledger technologies.
Objective O5: Safeguard openness to guarantee interoperability, accessibility, and international cooperation.
HOW: Establish a framework for social innovation with active co-creation and community engagement to maximize adoption. Through Eclipse Foundation Europe, adopt open-science practices and build channels to open-source communities. Run five pilot use cases (Manufacturing, Automotive, Agriculture, Energy, Healthcare) in five countries (one led by a Korean partner). Involve innovative SMEs aligned with consortium skills. Provide open-source snippets and core functions to accelerate external development. Align with EU Open Research principles to stimulate a vibrant, international HYPER-AI community and uptake.
Objective O6: Demonstrate HYPER-AI’s usability, performance, energy efficiency, interoperability, and impact.
HOW: Assess and showcase tools, modules, and the back-end optimization stack across five use cases at multiple pilot sites and scales. Monitor all scenarios under a rigorous evaluation framework. Focus on verifying interoperability in challenging operational environments (Manufacturing, Agriculture, Automotive, Energy, Healthcare).
Objective O7: Drive wide communication, efficient dissemination, and vertical exploitation of results.
HOW: Propose novel, disruptive business models arising from HYPER-AI, analyze market uptake, and offer revised “business-as-usual” models for diverse stakeholders (cloud vendors, policy-makers, telecoms/ISPs, developers, end-users) across the continuum. Explore incentivized, secure sharing of edge resources (including data) to grow Europe’s data economy, support EU single-market data spaces, and foster a trustworthy AI ecosystem. Evaluate Robot-as-a-Service (RaaS)—subscription-based access to robots with updates/maintenance within hybrid cloud-edge settings—and cloud robotics as potential models for the hybrid adaptive and cognitive computing continuum.
WP1 has been concluded successfully. All deliverables and progress reports were submitted intime. All payments were executed according to the CA. Risks registry is being maintained and periodically updated. Steering Committee (WP leaders) meetings were held at a monthly basis to monitor the project's progress. Technical integration meetings were also held at a monthly basis to proactively track the technical developments and integration activities foreseen in WP7.
WP2 has not started yet.
WP3 has been concluded successfully. A comprehensive literature review analysis was conducted to examine all aspects of the HYPER-AI project. Functional and non-functional requirements were collected and consolidated into a set of 28 requirements. The five use cases were fully defined, each detailing specific goals, operational scenarios, and expected impacts. A comprehensive set of key performance indicators (KPIs) and an evaluation framework were also defined. Finally, the architectural specifications of the HYPER-AI platform were finalized.
WP4 has been concluded successfully. Data models for computational resources and applications were designed. The Hypertool for Native nodes and the Open connectors for Device nodes were implemented. The Application Manager was designed and implemented.
WP5 is considered 50% complete because the core problems have been clearly identified and the fundamental mechanisms have already been designed and validated in simulation. The next phase focuses on enhancing these mechanisms, integrating them into the full Kubernetes-based architecture, and preparing their deployment and evaluation on real infrastructures.
WP6 is considered 48% completed. The developments and tasks in WP6 are progressing as planned, with no significant delays identified so far. During this period, the main effort has been dedicated to progressing the core technical work. In the next phase, the focus will shift toward refinement and integration with the HYPER-AI platform.
WP7 has not started yet.
WP8 has not started yet.
WP9 has been concluded successfully. all planned Deliverables and Milestones have been successfully delivered across Tasks 9.1 to 9.5. This includes the full setup of the project's branding and communication channels, the execution of synergy and event activities, the definition of exploitation, business and IPR strategies, and the consolidation of standardisation and open-source outputs. All results have been formally captured in D9.1 D9.2 and D9.3 marking the complete fulfilment of WP9 objectives.
WP10 has not started yet.
WP2 has not started yet.
WP3 has been concluded successfully. A comprehensive literature review analysis was conducted to examine all aspects of the HYPER-AI project. Functional and non-functional requirements were collected and consolidated into a set of 28 requirements. The five use cases were fully defined, each detailing specific goals, operational scenarios, and expected impacts. A comprehensive set of key performance indicators (KPIs) and an evaluation framework were also defined. Finally, the architectural specifications of the HYPER-AI platform were finalized.
WP4 has been concluded successfully. Data models for computational resources and applications were designed. The Hypertool for Native nodes and the Open connectors for Device nodes were implemented. The Application Manager was designed and implemented.
WP5 is considered 50% complete because the core problems have been clearly identified and the fundamental mechanisms have already been designed and validated in simulation. The next phase focuses on enhancing these mechanisms, integrating them into the full Kubernetes-based architecture, and preparing their deployment and evaluation on real infrastructures.
WP6 is considered 48% completed. The developments and tasks in WP6 are progressing as planned, with no significant delays identified so far. During this period, the main effort has been dedicated to progressing the core technical work. In the next phase, the focus will shift toward refinement and integration with the HYPER-AI platform.
WP7 has not started yet.
WP8 has not started yet.
WP9 has been concluded successfully. all planned Deliverables and Milestones have been successfully delivered across Tasks 9.1 to 9.5. This includes the full setup of the project's branding and communication channels, the execution of synergy and event activities, the definition of exploitation, business and IPR strategies, and the consolidation of standardisation and open-source outputs. All results have been formally captured in D9.1 D9.2 and D9.3 marking the complete fulfilment of WP9 objectives.
WP10 has not started yet.