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Encyclopedia of GIS pp 371–372Cite as

Geosensor Networks, Estimating Continuous Phenonena

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Synonyms

Interpolation of continous geofields; Estimation predication; Phenomenon spatial field; Sample trial; Sensor networks; Energy constraint; Estimation, parametric; Estimation, non-parametric; Tiny aggreagation service; Distributed algorithm

Definition

Geosensor networks (GSN) are deployed to monitor different environmental phenomena over a spatiotemporal space. Many environmental phenomena (also called spatial fields), such as a temperature field or a gas concentration field in an open space, are spatiotemporally continuous within the spatiotemporal space. Individual sensor readings, however, are point samples taken at the physical location of the sensor nodes about the underlying phenomenon. Thus, neighboring sensor nodes' readings are likely similar.

For GSN, the challenge is to provide an accurate and precise estimation of all points of dynamic spatial field based on limited discrete point samples collected by the network. Since sensor nodes are energy and processing limited...

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Recommended Reading

  1. Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: TAG: a Tiny AGgregation service for ad-hoc sensor networks. SIGOPS Operating Systems Review, 36, pp. 131–146. ACM Press, New York (2002)

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  2. Woo, A., Tong, T., Culler, D.: Taming the underlying challenges of reliable multihop routing in sensor networks. In: Proceedings of the First International Conference on Embedded networked sensor systems, California, USA, 4–7 Nov 2003, pp. 14–27. ACM Press, New York (2003)

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  3. Sharifzadeh, M., Shahabi, C.: Supporting spatial aggregation in sensor network databases. In: Proceedings of the 12th annual ACM international workshop on Geographic information systems, Washington DC, USA, 12–13 Nov 2004, pp. 166–17. ACM Press, New York (2004)

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  4. Hellerstein, J.M., Hong, W., Madden, S., Stanek, K.: Beyond average: Towards Sophisticated Sensing with Queries. In: Proceedings of the Second International Workshop on Information Processing in Sensor Networks, pp. 63–79, Palo Alto, CA, USA, 22–23 Apr 2003

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  5. Harrington, B., Huang, Y.: In-network surface simplification for sensor fields. In: Proceedings of the 13th annual ACM international workshop on geographic information systems, pp. 41–50, Bremen, Germany, 4–5 Nov 2005

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  6. Guestrin, C., Bodik, P., Thibaux, R., Paskin, M.A., Madden, S.: Distributed regression: an efficient framework for modeling sensor network data. In: Proceedings of the third international symposium on information processing in sensor networks, Berkeley, California, USA, 26–27 Apr 2004, pp. 1–10. ACM Press (2004)

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Author information

Authors and Affiliations

  1. Department of Spatial Information Science and Engineering, University of Maine, Orono, ME, USA

    Guang Jin

Authors
  1. Guang Jin
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Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA

    Shashi Shekhar (McKnight Distinguished University Professor) (McKnight Distinguished University Professor)

  2. Management Science and Information Systems Department, Rutgers Business School Newark and New Brunswick Rutgers, the State University of New Jersey, Newark, NJ, 07102, USA

    Hui Xiong (Assistant Professor) (Assistant Professor)

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© 2008 Springer-Verlag

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Jin, G. (2008). Geosensor Networks, Estimating Continuous Phenonena. In: Shekhar, S., Xiong, H. (eds) Encyclopedia of GIS. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35973-1_498

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