Today, data communication and processing in industrial plants usually assume well-known applications and carefully planned resources (processing, storage, network). This ensures quality of service, but limits flexibility. In the future, rapidly changing application scenarios can also be expected in the industrial environment, for example through the introduction of data analytics applications and the integration of sophisticated human-machine interfaces (such as augmented reality glasses for technicians).

Such applications combine high data rate and latency requirements with large-scale data processing tasks that must be delivered close to the point of use (end-to-end) due to latency requirements; in addition, these requirements may change significantly. Wireless transmission technologies are needed to support such applications. 5G systems can be the means of choice here, as they offer not only wireless transmission but also edge computing capabilities.

Management of different resources (processing, storage, network) that integrates them and makes them usable for the special requirements of industrial scenarios is missing so far. There is also no tool to support long-term resource planning and investment decisions (e.g., to invest in own spectrum or to purchase it from a public provider).

Goals of the thesis

Building on OMNeT++ (an extensible, modular, component-based C++ simulation library and framework, primarily for building network simulators) this work will investigate the deployment of dynamic, load- and application-dependent radio resource acquisition in simulation. More importantly, this thesis will evaluate the impact of planning recommendations that result from the given management approach (considering costs, priorities, …) and an application mix (VR tasks, offloading tasks, backups, …).

Roughly, the following steps will be performed:

• Implement an abstract model of the management- and planning tool in simulation
• Modeling of various scenarios that can occur in an industry environment
• Modeling of different action sequences to solve an arising load problem (e.g. stopping low priority tasks)
• Then, a quantitative evaluation of different recommendations for action and their impact on the industry use case shall be conducted and results are interpreted

Keywords

Simulation, 5G, industry 4.0

References

[1]: Nguyen, H. X. and Trestian, R. and To, D. and Tatipamula, M. , “Digital Twin for 5G and Beyond,” in IEEE Communications Magazine, vol. 59, no. 2, pp. 10-15, February 2021, doi: 10.1109/MCOM.001.2000343.

[2]: Sisinni, E. and Saifullah, A. and Han, S. and Jennehag, U. and Gidlund, M., “Industrial Internet of Things: Challenges, Opportunities, and Directions,” in IEEE Transactions on Industrial Informatics, vol. 14, no. 11, pp. 4724-4734, Nov. 2018, doi: 10.1109/TII.2018.2852491.

[3]: Xu, L. D. and He, We. and Li, Si., “Internet of Things in Industries: A Survey,” in IEEE Transactions on Industrial Informatics, vol. 10, no. 4, pp. 2233-2243, Nov. 2014, doi: 10.1109/TII.2014.2300753.