Industrial Communication Technology

5G4Industry: 5G networks for industrial use - from core to access

01.01.2021 bis 31.03.2023

The digitization of industrial processes causes increasing amounts of acquired data that needs to be send and processed within the production plant. However, the amount of data is not constant and thus generous resources are needed to handle the data peaks. 5G in combination with edge computing will create further degrees of freedom to distribute the control functionalities and thereby increase the flexibility of machines and production plants.

The aim of the project is the development and evaluation of autonomous management tools for the ressource types network, storage and compute. The tools shall be able to have an immediate control of the ressources in case of a system outage as well as day-ahead planning of maintenance tasks and a long term planning of ressoure investions in order to fulfill all task and enable all services. Priority based decisions can be made to protect the most important services against outages.

At inIT, we investigate networked real-time systems for industrial information technologies with emphasis on the end-to-end quality of service and criteria such as real-time capability, robustness and reliability from a user perspective. The solutions for the 5G connectivity are developed for realistic use cases. Especially, the integration of 5G for small and medium industrial companies is studied and demonstrated in the SmartFactoryOWL.

This project is promoted by:
Ministerium für Wirtschaft, Innovation, Digitalisierung und Energie des Landes Nordrhein-Westfalen (MWIDE NRW)
Sponsors: Projektträger Jülich
Funding Code: 005-2008-0061
Funding Lines: Wettbewerb 5G.NRW
Stakeholders / Contacts: Dipl.-Ing. Arne Neumann
Dipl.-Ing. Arne Neumann, Marvin Illian, Tobias Hardes, Lukas Martenvormfelde, B. Sc., Prof. Dr.-Ing. Lukasz Wisniewski, Prof. Dr.-Ing. Jürgen Jasperneite
An Architecture Concept for Short- and Long-term Resource Planning in the Industry 4.0 Environment
In: 18th IEEE International Conference on Factory Communication Systems (WFCS 2022), Apr 2022
Multi-Agent Deep Reinforcement Learning For Real-World Traffic Signal Controls - A Case Study
In: 20th International Conference on Industrial Informatics, INDIN, IEEE, Jul 2022
Bachelor
Implementation of an edge cluster orchestration tool for managing 5G applications in a campus network
Jan Philip Schonlau
29.08.2022 till 24.10.2022
Promoted by
Projektträger