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Dr.-Ing. Alexander Willner


IIoT Group Manager & Lecturer

Room 425
Einsteinufer 25
10587 Berlin



Curriculum Vitae

Dr. Alexander Willner is the head of the Industrial Internet of Things (IIoT) Center at the Fraunhofer Institute for Open Communication Systems (FOKUS) and the head of the IIoT research group at the chair of Next Generation Networks (AV) at the Technical University Berlin (TUB). In joint collaboration with the Berlin Center of Digital Transformation (LZDV) he is working with his groups in applying standard-based Internet of Things (IoT) technologies to industrial domains. With a focus on moving towards the realization of interoperable communication within the Industry 4.0, the most important research areas include industrial real-time networks (TSN), middleware systems (OPC UA), distributed AI (Digital Twins) and distributed Cloud Computing (Edge Computing) including management and orchestration.

Prior research positions include the University Bonn, he holds an M.Sc. and a Ph.D. (Dr.-Ing.) in computer science from the University Göttingen and the Technical University Berlin respectively. His research interests are on distributed information systems, linked data, communication middleware and service-oriented architectures. He is active in relevant standardization activities and alliances and gives a corresponding lecture at the Technical University Berlin; and in the past at the Humboldt University of Berlin as well.

At various occasions Dr. Willner also acts as ambassador for the science capital Berlin.

Tips for Students and Researchers


An Architectural Design for Measurement Uncertainty Evaluation in Cyber-Physical Systems
Citation key VonPilchau2020
Author von Pilchau, Wenzel Pilar and Gowtham, Varun and Gruber, Maximilian and Riedl, Matthias and Koutrakis, Nikolaos-Stefanos and Tayyub, Jawad and Hähner, Jörg and Eichstädt, Sascha and Uhlmann, Eckart and Polte, Julian and Frey, Volker and Willner, Alexander
Title of Book 15th Conference on Computer Science and Information Systems (FedCSIS)
Year 2020
Month aug
Abstract 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$\backslash$`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.
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