Skip to main content
Book cover

AGILE 2015 pp 145–163Cite as

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

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

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-16787-9_9
  • Chapter length: 19 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   119.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-16787-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   179.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Notes

  1. 1.

    http://www.mono-project.com.

References

  • Appice, A., Ciampi, A., Fumarola, F., & Malerba, D. (2014). Data mining techniques in sensor networks: Summarization, interpolation and surveillance. London: Springer.

    CrossRef  Google Scholar 

  • Armstrong, M. (1998). Basic linear geostatistics. Berlin: Springer.

    CrossRef  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Cressie, N. A. C. (1990). The origins of kriging. Mathematical Geology, 22(3), 1990.

    CrossRef  Google Scholar 

  • Cressie, N. A. C. (1993). Statistics for spatial data. New York: Wiley.

    CrossRef  Google Scholar 

  • Cressie, N. A. C., & Wikle, C. K. (2011). Statistics for spatio-temporal data. Hoboken: Wiley.

    Google Scholar 

  • Jaynes, E. T. (2003). Probability theory. Cambridge: Cambridge University Press.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Wackernagel, H. (2003). Multivariate geostatistics: An introduction with applications. Berlin: Springer.

    CrossRef  Google Scholar 

  • Walkowski, A. C. (2010). Modellbasierte Optimierung mobiler Geosensornetzwerke für raumzeitvariante Phänomene. Heidelberg: AKA Verlag.

    Google Scholar 

  • Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists (statistics in practice). West Sussex: Wiley.

    CrossRef  Google Scholar 

  • 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.

    Google Scholar 

  • Wikle, C. K. (2003). Hierarchical models in environmental science. International Statistical Review, 71(2), 181–199.

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Peter Lorkowski .

Editor information

Editors and Affiliations

Rights and permissions

Reprints 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