Abstract
Information acquisition in a pervasive sensor network is often affected by faults due to power outage at nodes, wrong time synchronizations, interference, network transmission failures, sensor hardware issues or excessive energy consumption for communications. These issues impose a trade-off between the precision of the measurements and the costs of communication and processing which are directly proportional to the number of sensors and/or transmissions. We present a spatio-temporal interpolation technique which allows an accurate estimation of sensor network missing data by computing the inverse distance weighting of the trend cluster representation of the transmitted data. The trend-cluster interpolation has been evaluated in a real climate sensor network in order to prove the efficacy of our solution in reducing the amount of transmissions by guaranteeing accurate estimation of missing data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ciampi, A., Appice, A., Malerba, D.: Summarization for Geographically Distributed Data Streams. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds.) KES 2010, Part III. LNCS, vol. 6278, pp. 339–348. Springer, Heidelberg (2010)
Ciampi, A., Appice, A., Malerba, D., Guccione, P.: Trend cluster based compression of geographically distributed data streams. In: CIDM 2011, pp. 168–175. IEEE (2011)
Draper, N.R., Smith, H.: Applied regression analysis. Wiley (1982)
Fabbris, L.: Statistica multivariata. McGraw-Hill (1997)
Guccione, P., Ciampi, A., Appice, A., Malerba, D.: Trend cluster based interpolation everywhere in a sensor network. In: Proceedings of the 2012 ACM Symposium on Applied Computing, Data Stream, ACM SAC(DS) 2012 (2012)
S.A.A. Temperature, http://climate.geog.udel.edu/climate/html_pages/sa_air_clim.html
Tomczak, M.: Spatial interpolation and its uncertainty using automated anisotropic inverse distance weighting (IDW) - cross-validation/jackknife approach. Journal of Geographic Information and Decision Analysis 2(2), 18–30 (1998)
Kim, B., Tsiotras, P.: Image segmentation on cell-center sampled quadtree and octree grids. In: SPIE Electronic Imaging / Wavelet Applications in Industrial Processing VI (2009)
Willett, R., Martin, A., Nowak, R.: Backcasting: A new approach to energy conservation in sensor networks. In: Information Processing in Sensor Networks, IPSN 2004 (2003)
Yong, J., Xiao-Ling, Z., Jun, S.: Unsupervised classification of polarimetric sar image by quad-tree segment and svm. In: 1st Asian and Pacific Conference on Synthetic Aperture Radar, APSAR 2007, pp. 480–483 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ciampi, A., Appice, A., Guccione, P., Malerba, D. (2012). Integrating Trend Clusters for Spatio-temporal Interpolation of Missing Sensor Data. In: Di Martino, S., Peron, A., Tezuka, T. (eds) Web and Wireless Geographical Information Systems. W2GIS 2012. Lecture Notes in Computer Science, vol 7236. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29247-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-29247-7_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29246-0
Online ISBN: 978-3-642-29247-7
eBook Packages: Computer ScienceComputer Science (R0)