Software Campus: AnKSeK – Anonymization techniques for automatic calibration of distributed sensor systems

April 8th, 2015  |  Published in Research

In order to meet the increasing demands for high resolution and high quality environmental data, e.g. in Smart City scenarios, new approaches for sensing systems have to be considered. One alternative for using few professional measuring stations consists in crowdsourcing the data collection – Participatory Sensing. Data is collected highly distributed with mobile and inexpensive sensors such as smart phones carried by private individuals. While a higher coverage is made possible at a lower cost, data quality is also lower initially. Often there is a combination of spatially poorly resolved reference data with high quality and well-resolved data with poor quality from mobile devices. Thus, one of the biggest challenges is the (re)calibration of the sensors to improve the data quality. One possibility is a collaborative calibration approach. If multiple sensors measure the same physical phenomenon simultaneously, the actual physical value and subsequently the deviation of the individual sensors can be calculated.

In order to achieve good calibration results, accurate context information is necessary, often consisting of location traces, timestamps and other attributes. As these data are contributed by individuals, joining them together results in a high potential for abuse and thus privacy implications. The sensor values ​​and the meta data allow direct conclusions about the participants. It is important that an effective data protection is not ignored, for example by the use of anonymization process.

The main goal of this Software Campus project is to explore the influence of the degree of anonymity on the calibration accuracy. Various existing privacy-preserving methods as well as calibration techniques are analyzed and guidelines for reasonable combinations are considered.



  • 04/2015 – 03/2016


  • Software AG


  • Participatory Sensing
  • Privacy
  • Distributed Calibration



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