“This framework will be completely open source and specifically designed to be implemented and deployed in Europe”
TECNALIA is developing an open source programming framework that enables decentralised and secure collaboration between Edge devices
TECNALIA’s goal is to develop an open source programming framework that enables decentralised and secure collaboration between Edge devices. To do so, it uses advanced technologies such as AI accelerators and machine learning (FPGAs, SNNs, Quantum), as well as a federated identity process that ensures device privacy.
Despite the distributed and heterogeneous nature of cloud computing, open management frameworks have so far not been sufficiently developed. Current solutions, such as hybrid core/edge management options, have limitations, and are not suitable for private deployments.
Accessibility is a crucial aspect, especially for researchers, who need intuitive user interfaces both for data management and for the selection and optimisation of machine learning/AI algorithms. Unfortunately, Edge orchestration solutions have not yet adequately addressed this aspect.
Integration of core infrastructure with smart devices
The OASEES project focuses on developing a comprehensive solution that brings together both core infrastructure and smart devices. This approach will allow the full potential of processing on Edge nodes to be achieved, overcoming the limitations of current solutions and providing a more effective response to users’ needs, especially in terms of accessibility and efficient management of data and algorithms.
Use cases
- E-Health: The end result of this pilot project will be the creation of an intelligent device capable of sensing, recording and analysing patients’ exercises set by their therapists. It will also provide smart, adaptive and personalised guidance on rhythm and intonation for Parkinson’s patients.
- Energy: The real-field pilot will demonstrate the capability of coordinate the management of fleets of electric vehicles, ensuring that their recharging is synchronised with the excess production of renewable energies in the DSOs (Distribution System Operators). These vehicles will be efficiently coordinated and programmed through the OASEES SDK and the corresponding orchestration platform.
- Hard-to-reach infrastructure: Autonomous inspection of various objects in this type of infrastructure (such as telecommunications towers) is carried out by drones operating in SWARM. These drones use dynamic route planning optimised through a quantum processor (QPU), which efficiently solves the travelling salesman problem (TSP). Symbiotic Natural Neural Systems (SNNS) are also used for fast and energy-efficient detection of objects and patterns during inspection.
- Critical infrastructure: Intelligent assessment of the structural safety of buildings using sensor devices installed both inside and around structures to carry out constant monitoring, collecting data related to structural integrity and any potential seismic activity. This data is processed by the Night Watch system: it uses AI and other algorithms to carry out checks, filters, corrections and interpretations of the raw sensor data.
- Industry 4.0: Collaborative robotic automation. A decentralised coordination mechanism will be implemented for all actors involved in the wood sanding process. This covers robotic systems, industrial and collaborative robots as well as human operators.
- Wind energy: Predictive maintenance in wind turbines will be achieved through the collection of acoustic samples using the BAMS (Wind Turbine Blade Acoustic Monitoring System). This process makes it possible to identify possible faults with the aim of improving the performance and reliability in the operation of wind turbines.
