AGILE 2015 pp 145-163
Towards Real-Time Processing of Massive Spatio-temporally Distributed Sensor Data: A Sequential Strategy Based on Kriging
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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.