Dynamic Adaptation of Campus Networks and Applications in Industrial Application Scenarios

Project Description

Development and Evaluation of Application Services for the Use of Robotics in Open Campus Networks

PROJECT GOALS

CampusDynA aims at the realization of applications from the fields of autonomous mobile robotics, resource efficiency of production facilities, and civil safety of production sites using open 5G campus networks, thereby contributing to the evaluation of the concrete added value of this technology for industrial application scenarios, which is critical for user acceptance. The focus is on aspects of mutual dynamic adaptation of network and application behavior (see use cases). The implications for performance improvements and innovations are analyzed both on the user and provider side as well as the broader societal impacts (sustainability, civil safety).

MARKET PERSPECTIVE

The OpenRAN approach offers the potential for a restructuring of communication systems or the creation of a market for new communication solutions and, likewise, for their applications. The open, standardized interfaces and the consistent virtualization not only of the OpenRAN components but also of the application components enable time and resource savings, leading to high acceptance of campus networks and their applications. The three application scenarios also demonstrate the wide range of applications based on dynamic network parameters and the potential for significant unique selling propositions.

INNOVATION & METHODOLOGY

Dynamic changes in the requirements for the network can rarely be accounted for in current campus network solutions without a cost-intensive overprovisioning. For example, novel edge-controlled autonomous transport systems (FTS) with a high point-specific bandwidth requirement could not be implemented using established network design methods. The first application scenario (S1) aims to significantly extend the limits of previous bandwidth restrictions through their spatially flexible allocation in the campus network. A comparable dynamic is also desirable with respect to latency in the network. This will be investigated in a second scenario (S2). Due to the restructuring of power generation in Germany, the maximum available (peak) capacity for increasingly smaller time periods is becoming predictable. Within S2, dynamic regulation of network latency should be realized, so that critical plant areas and process phases within the campus network can be captured in real time and adjusted by artificial intelligence. The third application scenario (S3) aims to extend the previously mentioned applications with the aspect of availability for prioritized use by third parties. In the event of a major incident in an industrial facility, a rapid response is urgently required. While regular operations are partially halted, the existing campus network infrastructure would largely remain unused.

OUR CONTRIBUTION

Gestalt Robotics is responsible for the coordination within the project as well as the development and evaluation of the application services
and acts as a service (Robotics/Navigation/Sensing as a Service) and data provider (AI datasets) regarding utilization.