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Provenance aware sensor networks for real-time data analysis

Lange, R-J. de (2010) Provenance aware sensor networks for real-time data analysis.

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Abstract:In environmental science, sensors are most commonly used for forecasting or monitoring environmental processes. The observations are usually collected on a per-project basis, therefore these measurements are often duplicated between projects running at multiple organizations. A step in the right way to avoid this duplication is to introduce sensor networks, as they not only allow researchers to perform real-time data analysis, but enable sensor data sharing as well. However, in order to draw accurate conclusions or validate new models using this automatically collected data, metadata needs to be stored that gives meaning to the recorded observations. The sensor data generated by a sensor network depends on several influences, like the configuration and location of the sensors or the aggregations performed on the raw measurements. This kind of metadata is called provenance data, as the origins of the data are recorded. In this thesis, the requirements of a provenance aware sensor network are collected and a workflow is proposed for recording and querying sensor data and their provenance. A prototype system implementing the workflow shows that the proposed approach can effectively process sensor data from several sources, of which the use is justified in scientific research as the data provenance is known as well
Item Type:Essay (Master)
Clients:
Eawag aquatic research
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science MSc (60300)
Link to this item:http://purl.utwente.nl/essays/59473
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