TY - THES A1 - König, Marvin T1 - ERROR ESTIMATION OF ACCELERATION SENSOR DATA IN IOT NETWORKS N2 - This research thesis contributes to the growing interest of wireless sensor networks (WSN)in smart factory environments. About 80% of the German machinery and plant construction industry intensively attend to the topic in order to increase factory uptime, provide individual customer solutions and improve customer service (Berger, 2017). The implementation of wireless sensor networks thereby arises new challenges, due to the high distribution of sensor units and the special requirement of low energy consumption while still providing sufficient network transmission capacities. Great research efforts are proceeded to investigate the behaviour of wireless sensor networks for specific applications and provide more comprehensive interfaces and protocols. This research study examines a specific use case of acceleration sensor nodes, which are used in cable carriers to predict likely maintenance times. Inaccurate and faulty measurements, transmission errors and different signal transformations make accurate predictions impossible. In that regard, various signal processing steps are conducted and evaluated in this thesis to compensate the resulting errors. Furthermore, a novel approach of estimating the remaining error through machine learning in form of a linear regression is conducted. The results show, that the margin of error can be sufficiently estimated by an appropriate reference measurement as target value for the model training. Results and findings as well as recommendation for further investigations are provided. Y1 - 2019 UR - https://opus4.kobv.de/opus4-hwr/frontdoor/index/index/docId/1856 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b721-opus4-18565 ER -