This Article 
 Bibliographic References 
 Add to: 
Toward Spatial Window Queries over Continuous Phenomena in Sensor Networks
April 2008 (vol. 19 no. 4)
pp. 559-571
Recent research on sensor networks has focused on efficient processing of declarative SQL queries over sensor nodes. Users are often interested in querying an underlying continuous phenomenon, such as a toxic plume, while only discrete readings of sensor nodes are available. Therefore, additional information estimation methods are necessary to process the sensor readings to generate the required query results. Most estimation methods are computationally intensive, even when computed in a traditional centralized setting. Furthermore, energy and communication constraints of sensor networks challenge the efficient application of established estimation methods in sensor networks. In this paper, we present an approach using Gaussian Kernel estimation to process spatial window queries over continuous phenomena in sensor networks. The key contribution of our approach is using a small number of Hermite coefficients to approximate the Gaussian Kernel function for sub-clustered sensor nodes. As a result, our algorithm reduces the size of messages transmitted in the network by logarithmic order, thus, saving resources while still providing high quality query results.

[1] S. Madden, M.J. Franklin, J.M. Hellerstein, and W. Hong, “TAG: A Tiny Aggregation Service for Ad-Hoc Sensor Networks,” ACM SIGOPS Operating Systems Rev., special issue, vol. 36, pp. 131-146, 2002.
[2] J.M. Hellerstein, W. Hong, S. Madden, and K. Stanek, “Beyond Average: Toward Sophisticated Sensing with Queries,” Proc. Second Int'l Workshop Information Processing in Sensor Networks (IPSN '03), pp. 63-79, 2003.
[3] A. Demers, J. Gehrke, R. Rajaraman, N. Trigoni, and Y. Yao, “The Cougar Project: A Work-in-Progress Report,” SIGMOD Record, vol. 32, no. 4, pp. 53-59, 2003.
[4] P. Bonnet, J. Gehrke, and P. Seshadri, “Querying the Physical World,” IEEE Personal Comm., vol. 7, pp. 10-15, 2000.
[5] G. Tolle, J. Polastre, R. Szewczyk, D. Culler, N. Turner, K. Tu, S. Burgess, T. Dawson, P. Buonadonna, D. Gay, and W. Hong, “A Macroscope in the Redwoods,” Proc. Third ACM Conf. Embedded Networked Sensor Systems (SenSys '05), pp. 51-63, 2005.
[6] J. Burrell, T. Brooke, and R. Beckwith, “Vineyard Computing: Sensor Networks in Agricultural Production,” IEEE Pervasive Computing, vol. 3, pp. 38-45, Jan. 2004.
[7] M. Duckham, S. Nittel, and M. Worboys, “Monitoring Dynamic Spatial Fields Using Responsive Geosensor Networks,” Proc. 13th Ann. ACM Int'l Workshop Geographic Information Systems (GIS '05), pp. 51-60, 2005.
[8] A. Deligiannakis, Y. Kotidis, and N. Roussopoulos, “Compressing Historical Information in Sensor Networks,” Proc. ACM SIGMOD '04, pp. 527-538, 2004.
[9] A. Jain, E.Y. Chang, and Y.-F. Wang, “Adaptive Stream Resource Management Using Kalman Filters,” Proc. ACM SIGMOD '04, pp.11-22, 2004.
[10] M. Sharifzadeh and C. Shahabi, “Supporting Spatial Aggregation in Sensor Network Databases,” Proc. 12th Ann. ACM Int'l Workshop Geographic Information Systems (GIS '04), pp. 166-175, 2004.
[11] F. Aurenhammer and Klein, , Handbook of Computational Geometry, first ed., chapter 5, Elsevier, Jan. 2000.
[12] B. Harrington and Y. Huang, “In-Network Surface Simplification for Sensor Fields,” Proc. 13th Ann. ACM Int'l Workshop Geographic Information Systems (GIS '05), pp. 41-50, 2005.
[13] W. Xue, Q. Luo, L. Chen, and Y. Liu, “Contour Map Matching for Event Detection in Sensor Networks,” Proc. ACM SIGMOD '06, pp. 145-156, 2006.
[14] C. Guestrin, P. Bodík, R. Thibaux, M.A. Paskin, and S. Madden, “Distributed Regression: An Efficient Framework for Modeling Sensor Network Data,” Proc. Third Int'l Symp. Information Processing in Sensor Networks (IPSN '04), pp. 1-10, 2004.
[15] V. Delouille, R. Neelamani, and R.G. Baraniuk, “Robust Distributed Estimation in Sensor Networks Using the Embedded Polygons Algorithm.,” Proc. Third Int'l Symp. Information Processing in Sensor Networks (IPSN '04), pp. 405-413, 2004.
[16] J. Racine, “Parallel Distributed Kernel Estimation,” Computational Statistics and Data Analysis, vol. 40, no. 2, pp. 293-302, 2002.
[17] M. Broadie and Y. Yamamoto, “Application of the Fast Gauss Transform to Option Pricing,” Management Science, vol. 49, no. 8, pp. 1071-1088, 2003.
[18] C. Yang, R. Duraiswami, N.A. Gumerov, and L. Davis, “Improved Fast Gauss Transform and Efficient Kernel Density Estimation,” Proc. Ninth IEEE Int'l Conf. Computer Vision (ICCV '03), pp. 464-471, 2003.
[19] L. Greengard and J. Strain, “The Fast Gauss Transform,” SIAM J.Scientific and Statistical Computing, vol. 12, no. 1, pp. 79-94, 1991.
[20] B.J.C. Baxter and G. Roussos, “A New Error Estimate of the Fast Gauss Transform,” SIAM J. Scientific Computing, vol. 24, no. 1, pp.257-259, 2002.
[21] O. Szász, “On the Relative Extrema of the Hermite Orthogonal Functions,” J. Indian Math. Soc., vol. 25, pp. 129-134, 1951.
[22] M. Bern and D. Eppstein, Approximation Algorithms for Geometric Problems, PWS Publishing Co., pp. 296-345, 1997.
[23] J. Han and M. Kamber, Data Mining: Concepts and Techniques, first ed. Morgan Kaufmann, 2001.
[24] O. Younis and M.-S. Fahmy, “Heed: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks,” IEEE Trans. Mobile Computing, vol. 3, no. 4, pp. 366-379, 2004.
[25] W.B. Heinzelman, A.P. Chandrakasan, and H. Balakrishnan, “An Application-Specific Protocol Architecture for Wireless Microsensor Networks,” IEEE Trans. Wireless Comm., vol. 1, no. 4, pp. 660-670, 2002.
[26] G. Jin and S. Nittel, “UDC: A Self-Adaptive Uneven Clustering Protocol for Dynamic Sensor Networks,” Proc. Int'l Conf. Mobile Ad-Hoc and Sensor Networks (MSN), 2005.
[27] N. Trigoni, Y. Yao, A.J. Demers, J. Gehrke, and R. Rajaraman, “Multi-Query Optimization for Sensor Networks,” Proc. First IEEE Int'l Conf. Distributed Computing in Sensor Systems (DCOSS '05), pp.307-321, 2005.
[28] R. Nowak and U. Mitra, “Boundary Estimation in Sensor Networks: Theory and Methods,” Proc. Second Int'l Workshop Information Processing in Sensor Networks (IPSN '03), pp. 80-95, 2003.
[29] N. Trigoni, Y. Yao, A.J. Demers, J. Gehrke, and R. Rajaraman, “Multi-Query Optimization for Sensor Networks,” Technical Report TR2005-1989, Cornell Univ., 2005.
[30] A. Bucklina, P.H. Wiebeb, S.B. Smolenacka, N.J. Copleyc, and M.E. Clarke, “Integrated Biochemical, Molecular Genetic, and Bioacoustical Analysis of Mesoscale Variability of the Euphausiid Nematoscelis dfficilis in the California Current,” Deep-Sea Research, vol. 49, pp. 437-462, 2002.
[31] /, 2007.
[32] O.V. Lepskii and V. Spokoiny, “Optimal Pointwise Adaptive Methods in Nonparametric Estimation,” technical report, undated.
[33] V.C. Raykar and R. Duraiswami, “Fast Optimal Bandwidth Selection for Kernel Density Estimation,” Proc. Sixth SIAM Int'l Conf. Data Mining (SDM '06), pp. 524-528, 2006.
[34] J. Hellerstein and W. Wang, “Optimization of In-Network Reduction,” Proc. Third Int'l Workshop on Data Management for Sensor Networks (DMSN '04), pp. 166-175, 2004.

Index Terms:
Wireless sensor networks, spatial databases, distributed databases, query processing
Guang Jin, Silvia Nittel, "Toward Spatial Window Queries over Continuous Phenomena in Sensor Networks," IEEE Transactions on Parallel and Distributed Systems, vol. 19, no. 4, pp. 559-571, April 2008, doi:10.1109/TPDS.2007.70741
Usage of this product signifies your acceptance of the Terms of Use.