Hyper-Distributed Artificial Intelligence Platform for Network Resources Automation and Management Towards More Efficient Data Processing Applications

Digital, Industry & SpaceHORIZON-RIAID: 101135982
EC Contribution
โ‚ฌ46,290
Consortium Size
15 orgs
Start Year
2024
โ–ถSummary

In HYPER-AI, we work with smart virtual computing entities (nodes) that come from a variety of infrastructures that span all three of the so-called computing continuum's layers: the Cloud, the Edge, and IoT.It focuses on intensive data-processing applications that present the potential to improve their footprint when hyper-distributed in an optimized manner. In order to give targeted applications access to computational, storage, or network services, HYPER-AI implements the idea of computing swarms as autonomous, self-organized, and opportunistic networks of smart nodes. These networks may offer a diverse and heterogeneous set of resources processing, storage, data, communication) at all levels and have the ability to dynamically connect, interact, and cooperate. HYPER-AI proposes semantic representation concepts to enable heterogeneous resourcesโ€™ abstraction in a homogeneous way, under a common annotation (computing node), across the whole range of network infrastructures. The main orchestration and control concept of HYPER-AI is inspired by autonomic systems (self-CHOP principles) which employ swarmed computing schemes. Its objective is to make smart multi-node (swarm) deployment scenario design, execution, and monitoring easier, through appropriate AIs for self-configuration (nodes assigned resources), self-healing (swarmed nodes lifecycle), self-optimizing (exploiting built-in situation awareness mechanisms) and self-protecting (intrusion detection, privacy, security, encryption and identity management) at application runtime. In order to support dynamic and data-driven application workflows, HYPER-AI suggests the flexible integration of resources at the edge, the core cloud, and along the big data processing and communication channel, enabling their energy, time and cost-efficient execution. Finally, distributed ledger concepts for security, privacy, and encryption as well as AI-based intrusion detection are also considered.

Consortium (15)

Project Results (13)

Source: CORDIS, the EU research results database.

โ–ถPublications (3)
Development and Comparison of Artificial Neural Networks and Gradient Boosting Regressors for Predicting Topsoil Moisture Using Forecast Data
AIยท 2025DOI
Miriam Zambudio Martรญnez, Larissa Haringer Martins da Silveira, Rafael Marin-Perez, Antonio Fernando Skarmeta Gomez
Lightweight authenticated key exchange for low-power IoT networks using EDHOC
Internet of Thingsยท 2025DOI
Alejandro Arias-Jimenez, Jorge Gallego-Madrid, Jesus Sanchez-Gomez, Rafael Marin-Perez
Reducing IoT Data for Highly Efficient Cloud Storage
Procedia Computer Scienceยท 2025DOI
Felix Safaridis, Eleftheria Katsarou, Stathes Hadjiefthymiades
โ–ถDeliverables (10)