Qualitative Spatio-temporal Reasoning from Mobile Sensor Data using Qualitative Trigonometry

Chapter

Abstract

This paper presents a method for qualitative spatio-temporal reasoning about dynamic spatial regions from mobile sensor data based on qualitative trigonometry. We apply this method to the use case of monitoring a travelling gas plume with a mobile sensor. We argue that our method can infer qualitative information about size, movement direction, and speed of a spatial region from the data of a mobile sensor passing through it, which allows for adapting the route of the sensor that captures the phenomenon in space and time.

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References

  1. Cohn, A.G. and Renz, J. (2008) Qualitative spatial representation and reasoning. Foundations of Artificial Intelligence, 3:551-596.CrossRefGoogle Scholar
  2. Bian, L. (2007) Object-Oriented Representation of Environmental Phenomena: Is Everything Best Represented as an Object? Annals of the Association of American Geographers, 97(2):267-281.CrossRefGoogle Scholar
  3. Duckham, M., Nittel S., and Worboys M. (2005). Monitoring dynamic spatial fields using responsive geosensor networks. In Proceedings of the 13th annual ACM international workshop on Geographic information systems, pages 51- 60. ACM.Google Scholar
  4. Duckham, M., Stell, J., Vasardani, M., and Worboys, M. (2010) Qualitative change to 3-valued regions. In Sara Fabrikant, Tumasch Reichenbacher, Marc van Kreveld, and Christoph Schlieder, editors, Geographic Information Science, volume 6292 of Lecture Notes in Computer Science, pages 249-263. Springer Berlin / Heidelberg.Google Scholar
  5. Dutta, S. (1990) Qualitative spatial reasoning: A semi-quantitative approach using fuzzy logic. In Design and Implementation of Large Spatial Databases, volume 409 of Lecture Notes in Computer Science, pages 345-364. Springer Berlin / Heidelberg.Google Scholar
  6. Frank, A.U. (1996) Qualitative spatial reasoning: Cardinal directions as an example. International Journal of Geographical Information Science, 10(3):269-290.Google Scholar
  7. Freksa, C. (1992) Using orientation information for qualitative spatial reasoning. In Theories and Methods of Spatio-Temporal Reasoning in Geographic Space, volume 639 of Lecture Notes in Computer Science, pages 162-178. Springer Berlin / Heidelberg.Google Scholar
  8. Giostra, U. (1994) Dynamical models of pollutant transport in the atmosphere. Aerobiologia, 10(1):53-57, Springer Netherlands.Google Scholar
  9. Jiang, J. and Worboys, M. (2008) Detecting basic topological changes in sensor networks by local aggregation. In Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems, pages 1-10. ACM.Google Scholar
  10. Liu, J. (1998) A method of spatial reasoning based on qualitative trigonometry. Artificial Intelligence, 98(1-2):137-168.CrossRefGoogle Scholar
  11. Mallet, V., Quélo, D., Sportisse, B., Ahmed de Biasi, M., Debry, É., Korsakissok, I., Wu, L., Roustan, Y., Sartelet, K., Tombette, M., et al. (2007) Technical Note: The air quality modeling system Polyphemus. Atmospheric Chemistry and Physics Discussions, 7(3):6459-6486.CrossRefGoogle Scholar
  12. Patan, M. and Uciski, D. (2004) Robust activation strategy of scanning senors via sequential design in parameter estimation of distributed systems. In Parallel Processing and Applied Mathematics, volume 3019 of Lecture Notes in Computer Science, pages 770-778. Springer Berlin / Heidelberg.Google Scholar
  13. Shi, M. and Winter, S. (2010) Detecting change in snapshot sequences. In Sara Fabrikant, Tumasch Reichenbacher, Marc van Kreveld, and Christoph Schlieder, editors, Geographic Information Science, volume 6292 of Lecture Notes in Computer Science, pages 219-233. Springer Berlin / Heidelberg.Google Scholar
  14. Song, Z., Chen, Y.Q., Liang, J.S., and Ucinski, D. (2007) Optimal mobile sensor motion planning under non-holonomic constraints for parameter estimation of distributed systems. International Journal of Intelligent Systems Technologies and Applications, 3(3):277-295.CrossRefGoogle Scholar
  15. Walkowski, A. C. (2008) Model based optimization of mobile geosensor networks. In The European Information Society, Lecture Notes in Geoinformation and Cartography, pages 51-66. Springer Berlin / Heidelberg, 2008.Google Scholar
  16. Worboys, M. and Duckham, M. (2006) Monitoring qualitative spatiotemporal change for geosensor networks. International Journal of Geographical Information Science, 20(10):1087-1108.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Institute for GeoinformaticsUniversity of MuensterMuensterGermany

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