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Concepts such as Industry 4.0 and Industrial Internet of Things aim for a digital transformation of manufacturing companies. One aspect that is to be transformed is the shop floor, where the interconnection of machines and sensor devices plays a major role. 5G positions itself to be a key technology to enable this transformation by providing reliable, low latency, and high bandwidth communication which is required by industrial use cases. However, so far it is still unclear which role 5G will play in industrial networks and how it can be integrated into existing factory ecosystems. Therefore, this paper presents three different data flow architectures that illustrate how 5G can be integrated into the production IT and how it can coexist with a factory cloud system. Furthermore, we describe a use case that reflects typical industrial communication requirements and will be used in the future to evaluate the different architectures. We conclude by presenting a validation plan and an outlook on future work.

For over a decade researchers have been reporting the impact of self-similar long-range dependent network traffic. Long-range dependence (LRD) is of great significance in traffic engineering problems such as measurement, queuing strategy, buffer sizing and admission and congestion control. In this research, in order to determine the existence of LRD, we apply three different robust versions of the autocorrelation function (ACF), namely weighted ACF (WACF), trimmed ACF (TACF) and variance-ratio of differences and sums, known as the D/S variance estimator (DACF), in conjunction with the sample ACF (which is moment based). Here we define the moment based ACF as MACF. In telecommunications, LRD traffic defines that a similar pattern of traffic persists for a longer span of time. Through ACF, it is possible to detect how long the traffic lasts. The aim of this research is to investigate the performance of ACF in identifying the existence of LRD traffic.