Jürgen Pfeifer is IoT & Cloud Partner Manager at Wago Kontakttechnik. He studied electrical engineering and electronics at the University of Siegen and started his career as a project engineer in automation technology. After joining Wago in 2001, he held various positions in the company.

The Industrial Internet of Things is generating more and more data that needs to be managed. This data complexity needs to be mastered. What role does edge computing play in this?

Pfeifer: Edge computing can be easy on the wallet in terms of 'more data'. Many cloud providers earn their money with data per unit of time. If data is filtered in advance at the edge or, for example, data is only transferred to the cloud in the event of changes and changes of value, this can significantly reduce the financial burden. Two different philosophies can be observed with regard to 'data complexity': In one variant, the harmonization of the data, i.e. the transfer of the data into a data model that applies to the company or the use case, takes place directly at the point of origin, while in the other variant, the data is only transferred to the cloud. The Edge is particularly well suited for the first variant directly at the point of origin, for example directly in the control cabinet next to the PLC on the machine.

Sensor data is preselected at the edge, software applications are processed or even AI calculations are carried out. What should users bear in mind with regard to the edge software landscape? Which technologies should/must be taken into account?

Pfeifer: The Edge connects the OT world, i.e. the control technology and PLC world, with the IT world. True to the tried and tested motto 'never change a running system', the PLC world is very static. IT can and must be very dynamic in some cases. Dynamic due to security issues, such as the maintenance of security patches, or due to the fact that digitalization in production is still in its infancy. Many new ideas for business models will emerge that require a new software application or a new optimized algorithm of an analytic to be loaded on the edge. If you rely on Linux and Docker and, if necessary, an x86 architecture, you can't go wrong from today's perspective.

The hardware requirements grow with the tasks. What should users look out for when choosing the right Edge Computing hardware?

Pfeifer: At some point, all hardware reaches its limits. In this context, the fact that load balancing can be carried out with a good orchestration tool for the Docker containers is very interesting. If the application allows it, the containers with the software applications can be distributed across several devices. It is then not the finiteness of the individual device that counts, but the device network or cluster. This network can then also include resources in the cloud and the finiteness is further relativized.