AerOS
Main goals of the project
- Design and build a virtualised, platform-agnostic, zero-touch orchestration (of resources & services) Meta Operating System for the IoT edge-cloud continuum.
- Achieve optimal orchestration through flexible containerised edge nodes and open APIs for quality, stability, and security.
- Use a holistic approach supporting data autonomy strategies and the development of industrial IoT communication networks.
- Maximise impact through global and cross-sectorial presence, considering individual use case scenarios and long-term market and sustainability trends.
Main outputs of the project
- Virtualised and platform-agnostic Meta Operating System.
- Methodology, standardisation, and protocols usable across the continuum for legacy and novel technologies.
- Data autonomy strategy for the CEI continuum.
- Infrastructural services and features for cybersecurity, trustworthiness
AerOS use cases
*Read more information on the project website.
High Performance Computing Platform for Connected & Cooperative Agricultural Mobile Machinery (Smart Tractors)
Methodology: Development of a cooperative, large-scale harvesting system using sensor data, secure cloudbased operating instructions, and a swarm of smart vehicles (tractors).
Objective: Optimization of a fully-electric vehicle swarm capable of securely and reactively performing precision farming; latency reduction through the implementation of edge computing; CO2-neutral farming capabilities by integrating frugal AI with CEI continuum
Containerised Edge Computing near Renewable Energy Sources
Methodology: Integration of CloudEdge processing with renewable energy sources and sensor data, monitoring of performance changes in shifting from Cloud-based to Edge-based processing, real-time adjustments of energy production activity based on analytical benchmarks.
Objective: Increase in energy supply cybersecurity through localization of data processing; Resource optimisation through analytical task automation; Increased system operability and stability through node pooling and adjustment failsafes.
Data-Driven Cognitive Production Lines
Methodology: Integration of cloud edge continuum with production lines, increased remote interaction between monitoring and intelligence tools and physical equipment, monitoring of energy efficiency and machine error.
Objective: Real-time error compensation resulting in Zero-Defect Manufacturing; Net-zero energy manufacturing and greater sustainability through production line optimisation; Advanced and secure production automation through integration of safety measures and reconfiguration options in CloudEdge IoT.
Energy Efficient, Health Safe & Sustainable Smart Buildings
Methodology: Integration of CloudEdge processing with sensors in an office setting (corporate building), application of self-managing systems related to health and efficiency, and implementation of data governance and identification mechanisms.
Objective: Effective clustering and conditions of employees based on sensor data; Decentralised room management through edge processes; Data privacy and cybersecurity optimisation through authentication and anonymisation mechanisms.
Smart edge services for the Port Continuum
Methodology: Integration of Cloud-Edge processing with port logistics chain, application of computer vision algorithms to container / ship / truck management, authentication and control tools localisation and securitisation.
Objective: Increased efficiency and reliability of local processes through increased cybersecurity and connectivity; Predictive maintenance and efficient management governed by continuum-based data and inputs; Risk and error prevention through automatic detection and alert-generation.