|Project Home:||AI-NET ANTILLASI|
|Duration:||3 years, 01.04.2021-31.03.2024|
|Partners:||NOKIA (DE, FR), Atesio GmbH (DE), Creonic GmbH (DE), TWT GmbH (DE), WIBU-SYSTEMS AG (DE), Fraunhofer FOKUS (DE), TU Berlin/DCAITI (DE), Universität Stuttgart (IKR) (DE), Universität Stuttgart (INT) (DE), Universität Erlangen-Nürnberg (FAU-IT) (DE), Ruhr-Universität Bochum (RUB-VS) (DE), Karlsruher Institut für Technologie (KIT) (DE), TU Kaiserslautern (TUK) (DE), Technische Universität München – LKN (DE), Helmut Schmidt Universität Hamburg (HSU) (DE), DFKI (DE), let's dev GmbH & Co. KG (DE), FARO Europe GmbH (DE), Alterway (FR), Gandi.net (FR), Umanis (FR), Institut Mines Telecom / Telecom Paris (FR), University Rennes 1 (FR), VectraWave (FR), INRIA (FR), CNRS (FR), VTT Technical Research Centre of Finland Ltd. (FI), Airbus Defence and Space Oy (FI), Centria (FI), F-Secure Corporation (FI), Insta DefSec Oy (FI), Eficode (FI), Xiphera (FI), Privecomms (FI), Goodmill Systems (FI), Huld (FI), Iprotoxi (FI), Riots (FI).|
Scope of the Project
AI-NET ANTILLAS has set itself the goal of developing new, innovative solutions for these problems on the basis of specific application scenarios. Intelligent end-to-end automation on the network, service and physical level should largely avoid manual process steps and enable fully autonomous network operation in the future. Different network levels should be considered across the board in order to achieve optimization across traditional boundaries.
The focus is on the development of a latency and security-optimized infrastructure for telecommunication networks, with the aim of enabling new applications in the areas of Industry 4.0 and teleoperated and networked driving. The non-functional properties such as flexibility, reconfigurability, energy efficiency and automation are derived directly from the use cases and form the basis for a cross-layer approach that is pursued in this project. The use cases are evaluated experimentally in realistic environments such as the ARENA2036 test platform in Stuttgart.
In this project, we plan to develop network reconfiguration and resource adaptation mechanisms in the context of dynamic SDN/NFV-enabled networks. In particular, we would like to study when the reconfiguration is needed (e.g., due to user mobility, traffic increase/decrease), and how these reconfigurations should be efficiently determined and implemented.
This work is partially funded by Federal Ministry of Education and Research in Germany (BMBF) as part of the project AI-NET-ANTILLAS (grant ID 16KIS1318).