Dynamic sample rate adaptation for long-term IoT sensing applications

Publikationstyp
Conference Paper
Date Issued
2017-02-06
Sprache
English
Author(s)
Kulau, Ulf  
Balen, Johannes van  
Schildt, Sebastian  
Büsching, Felix  
Wolf, Lars  
TORE-URI
http://hdl.handle.net/11420/10891
Start Page
271
End Page
276
Article Number
7845437
Citation
3rd IEEE World Forum on Internet of Things, WF-IoT 2016: 7845437, 271-276 (2017-02-06)
Contribution to Conference
3rd IEEE World Forum on Internet of Things, WF-IoT 2016  
Publisher DOI
10.1109/WF-IoT.2016.7845437
Scopus ID
2-s2.0-85015179320
Publisher
IEEE
In 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.
DDC Class
004: Informatik
Funding Organisations
Deutsche Forschungsgemeinschaft (DFG)  
More Funding Information
We would like to thank the VSD Dethlingen research station for providing the field and technical support. This research was partially supported by the German Research Council (DFG) under the grant no. BU 3282/2-1.