AGILE 2015 pp 145-163 | Cite as

Towards Real-Time Processing of Massive Spatio-temporally Distributed Sensor Data: A Sequential Strategy Based on Kriging

Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Sensor data streams are the basis for monitoring systems which infer complex information like the excess of a pollution threshold for a region. Since sensor observations tend to be arbitrarily distributed in space and time, an appropriate interpolation method is necessary. Within geostatistics, kriging represents a powerful and established method, but is computation intensive for large datasets. We propose a method to exploit the advantages of kriging while limiting its computational complexity. Large datasets are divided into sub-models, computed separately and merged again in accordance with their kriging variances. We apply the approach to a synthetic model scenario in order to investigate its quality and performance.

Keywords

Continuous phenomena Sensor data streams Spatio-temporal interpolation Kriging Deviation map 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute for Applied Photogrammetry and Geoinformatics (IAPG)Jade University of Applied Sciences Wilhelmshaven/Oldenburg/ElsflethOldenburgGermany

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