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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Appice, A., Ciampi, A., Fumarola, F., & Malerba, D. (2014). Data mining techniques in sensor networks: Summarization, interpolation and surveillance. London: Springer.
Armstrong, M. (1998). Basic linear geostatistics. Berlin: Springer.
Barillec, R., Ingram, B., Cornford, D., & Csató, L. (2011). Projected Sequential Gaussian Processes: a C++ tool for interpolation of large data sets with heterogeneous noise. Computers and Geosciences, 37 (2011), 295–309.
Botts, M., Percivall, G., Reed, C., & Davidson, J. (2007). OGC® sensor web enablement: Overview and high level architecture. Open geospatial consortium. Online document: http://portal.opengeospatial.org/files/?artifact_id=25562.
Cressie, N. A. C. (1985). Fitting variogram models by weighted least squares. Mathematical Geology, 17(5), 1985.
Cressie, N. A. C. (1990). The origins of kriging. Mathematical Geology, 22(3), 1990.
Cressie, N. A. C. (1993). Statistics for spatial data. New York: Wiley.
Cressie, N. A. C., & Wikle, C. K. (2011). Statistics for spatio-temporal data. Hoboken: Wiley.
Jaynes, E. T. (2003). Probability theory. Cambridge: Cambridge University Press.
Katzfuss, M., & Cressie, N. A. C. (2011). Tutorial on fixed rank kriging (FRK) of CO 2 data. Technical Report No. 858. Department of Statistics, The Ohio State University.
Osborne, M. A., Roberts, S. J., Rogers, A., & Jennings, I. R. (2012). Real-time information processing of environmental sensor network data using bayesian gaussian processes. ACM Transactions on Sensor Networks, 9(1), 1.
Romanowicz, R., Young, P., Brown, P., & Diggle, P. (2005). A recursive estimation approach to the spatio-temporal analysis and modeling of air quality data. Amsterdam: Elsevier.
Wackernagel, H. (2003). Multivariate geostatistics: An introduction with applications. Berlin: Springer.
Walkowski, A. C. (2010). Modellbasierte Optimierung mobiler Geosensornetzwerke für raumzeitvariante Phänomene. Heidelberg: AKA Verlag.
Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists (statistics in practice). West Sussex: Wiley.
Whittier, J. C., Nittel, S., Plummer, M. A., & Liang, Q. (2013). Towards window stream queries over continuous phenomena. In: 4th ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS), Orlando.
Wikle, C. K. (2003). Hierarchical models in environmental science. International Statistical Review, 71(2), 181–199.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Lorkowski, P., Brinkhoff, T. (2015). Towards Real-Time Processing of Massive Spatio-temporally Distributed Sensor Data: A Sequential Strategy Based on Kriging. In: Bacao, F., Santos, M., Painho, M. (eds) AGILE 2015. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-16787-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-16787-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16786-2
Online ISBN: 978-3-319-16787-9
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)