CANARIA - Cloud-enabled Aircraft Network and ARtificial Intelligence-based data Analysis

CANARIA - Cloud-enabled Aircraft Network and ARtificial Intelligence-based data Analysis (communication and computer network architecture for the networked aircraft of the future) aims at an innovative federated communication and computer network architecture for the networked aircraft of the future and integrates state-of-the-art wireless communication technologies.

CANARIA is developing (in a prototype implementation) a "Federated Data Management Platform", which represents a paradigm shift away from the hardware-centric view towards a flexible (because as far as possible software-driven), high-performance, virtualized platform for many new applications for cabin, aircraft operations and for passenger interaction/entertainment. This is also reflected in the main work objectives, where hardware for wireless communication and network components will be started first, then Software Defined Measurement and the Software Defined Network will be implemented based on that, and finally a virtual cloud environment for the CANARIA applications.

In the CANARIA-Spoof2x subproject, TUD-ITVS will test a Software Defined Measurement approach and the optimization of routing in the "Software Defined Network" based on it under deployment scenarios that are as real as possible.
For this purpose, an autonomous hybrid condition monitoring system for the aircraft cabin is developed, which combines a model-based load estimation with measured structural information (disturbances - jamming, spoofing, interference). The use of autonomous sensor nodes is envisioned to determine the structural information, which will transmit the information to the existing TriaGnoSys system via a wireless network. Damage analysis can then happen on the existing onboard IT assets. This architecture allows to keep installation efforts and system weight low. Integration into the existing aircraft bus architecture also allows the system and its data to be integrated into the "on-board" maintenance systems to act as an additional source of information when deciding on necessary maintenance actions.

Project sponsor:

Project advisor:

Luftfahrtforschung und –technologie des DLR

Project partners:

• TriaGnoSys GmbH
• Technische Universität Braunschweig - Institut für Flugführung
• Cadami GmbH
• Fraunhofer Institut für offene Kommunikationssysteme

Project duration:

1.12.2020 - 31.05.2023

Contact:

Robert Richter