As a cornerstone of most of modern technological achievements, the Internet of Things (IoT) is currently also transforming the landscape of the industrial sector by enabling various novel applications (e.g. condition/machine health monitoring, predictive maintenance, energy optimization). Intelligent algorithms in such Industrial Internet of Things (IIoT) systems  are typically fueled by a massive amount of data coming from diverse data sources (e.g. telemetry sensors, smart machines and devices). Due to this variety of different data sources and the sheer amount of data signals, the testing effort needs to be focused on data signals which are most likely to serve data with low quality . To address this need, a conceptual framework comprising of a set of criteria of data sources (e.g. mean uptime, type of power source (hardwired/battery), type of connectivity (wired/wireless)) was developed that aims to describe the probability that data sources in an IIoT environment provide data with low quality. In this thesis, the conceptual framework needs to be implemented as a software application.
Development of a software application that graphically represents the framework and hence can be used to assess data sources.
- Requirements and Design Specification
- Graphical User Interface
- Modeling of Data Sources
- Calculations of the Assessments
- Importing/Exporting Assessment Results
- Documentation (Focus: Future Modifiability)
- Software stack (VB, Java or by arrangement)
 Boyes et al. 2018: The industrial internet of things (IIoT): An analysis framework.
 Foidl, Felderer 2019: Risk-based data validation in machine learning-based software systems.