  Several use cases from the areas of manufacturing and process industry,
require highly accurate sensor data. As sensors always have some degree of
uncertainty, methods are needed to increase their reliability. The common
approach is to regularly calibrate the devices to enable traceability according
to national standards and Syst\`eme international (SI) units - which follows
costly processes. However, sensor networks can also be represented as Cyber
Physical Systems (CPS) and a single sensor can have a digital representation
(Digital Twin) to use its data further on. To propagate uncertainty in a
reliable way in the network, we present a system architecture to communicate
measurement uncertainties in sensor networks utilizing the concept of Asset
Administration Shells alongside methods from the domain of Organic Computing.
The presented approach contains methods for uncertainty propagation as well as
concepts from the Machine Learning domain that combine the need for an accurate
uncertainty estimation. The mathematical description of the metrological
uncertainty of fused or propagated values can be seen as a first step towards
the development of a harmonized approach for uncertainty in distributed CPSs in
the context of Industrie 4.0. In this paper, we present basic use cases,
conceptual ideas and an agenda of how to proceed further on.
