Kulau, UlfUlfKulauBalen, Johannes vanJohannes vanBalenSchildt, SebastianSebastianSchildtBüsching, FelixFelixBüschingWolf, LarsLarsWolf2021-11-112021-11-112017-02-063rd IEEE World Forum on Internet of Things, WF-IoT 2016: 7845437, 271-276 (2017-02-06)http://hdl.handle.net/11420/10891In long-term sensing applications data patterns can vary significantly over time. Often a multitude of sensors are used to measure different types of environmental conditions. Considering such variations it is hard to select a predefined sample rate that guarantees both, high data quality and energy efficiency. Hence, this paper presents a dynamic sample rate adaptation that strikes a balance offering optimal energy efficiency while maintaining high data quality. Based on the general concept of Bollinger Bands, a metric is derived that solely depends on the trend of the measured data itself. A real world measurement in the area of smart farming is used to show the effectiveness of this approach.enInformatikConference Paper10.1109/WF-IoT.2016.7845437Other