skip to main content
article

Multiresolution storage and search in sensor networks

Published:01 August 2005Publication History
Skip Abstract Section

Abstract

Wireless sensor networks enable dense sensing of the environment, offering unprecedented opportunities for observing the physical world. This article addresses two key challenges in wireless sensor networks: in-network storage and distributed search. The need for these techniques arises from the inability to provide persistent, centralized storage and querying in many sensor networks. Centralized storage requires multihop transmission of sensor data to Internet gateways which can quickly drain battery-operated nodes.Constructing a storage and search system that satisfies the requirements of data-rich scientific applications is a daunting task for many reasons: (a) the data requirements may be large compared to available storage and communication capacity of resource-constrained nodes, (b) user requirements are diverse and range from identification and collection of interesting event signatures to obtaining a deeper understanding of long-term trends and anomalies in the sensor events, and (c) many applications are in new domains where a priori information may not be available to reduce these requirements.This article describes a lossy, gracefully degrading storage model. We believe that such a model is necessary and sufficient for many scientific applications since it supports both progressive data collection for interesting events as well as long-term in-network storage for in-network querying and processing. Our system demonstrates the use of in-network wavelet-based summarization and progressive aging of summaries in support of long-term querying in storage and communication-constrained networks. We evaluate the performance of our linux implementation and show that it achieves: (a) low communication overhead for multiresolution summarization, (b) highly efficient drill-down search over such summaries, and (c) efficient use of network storage capacity through load-balancing and progressive aging of summaries.

References

  1. Cerpa, A., Elson, J., Estrin, D., Girod, L., Hamilton, N., and Zhao, J. 2001. Habitat monitoring: Application driver for wireless communications technology. In Proceedings of the 2001 ACM SIGCOMM Workshop on Data Communications in Latin America and the Caribbean. Google ScholarGoogle Scholar
  2. Cerpa, A. and Estrin, D. 2002. Ascent: Adaptive self-configuring sEnsor networks topologies. In Proceedings of the IEEE Infocom. New York, NY.Google ScholarGoogle Scholar
  3. Chakrabarti, K., Garofalakis, M., Rastogi, R., and Shim, K. 2001. Approximate query processing using wavelets. VLDB J. 10, 2--3, 199--223. Google ScholarGoogle Scholar
  4. Davis, G. Wavelet Image Compression Kit.Google ScholarGoogle Scholar
  5. Ganeriwal, S., Han, C.-C., and Srivastava, M. B. 2003. Going beyond nodal aggregates: Spatial average of a continuous physical process in sensor networks. Poster in Sensys 2003. To appear.Google ScholarGoogle Scholar
  6. Ganesan, D., Estrin, D., and Heidemann, J. 2002. Dimensions: Why do we need a new data handling architecture for sensor networks? In 1st Workshop on Hot Topics in Networks (Hotnets-I). Vol. 1.Google ScholarGoogle Scholar
  7. Ganesan, D., Greenstein, B., Perelyubskiy, D., Estrin, D., and Heidemann, J. 2003. Multi-resolution storage in sensor networks. In Proceedings of the 1st ACM Conference on Embedded Networked Sensor Systems (SenSys). Google ScholarGoogle Scholar
  8. Girod, L., Stathopoulos, T., Ramanathan, N., Elson, J., Estrin, D., Osterweil, E., and Schoellhammer, T. 2004. A system for simulation, emulation, and deployment of heterogeneous sensor networks. In Proceedings of the 2nd ACM Conference on Embedded Networked Sensor Systems. Baltimore, MD. Google ScholarGoogle Scholar
  9. Greenstein, B., Estrin, D., Govindan, R., Ratnasamy, S., and Shenker, S. 2003. Difs: A distributed index for features in sensor networks. Elsevier J. Ad Hoc Netw.Google ScholarGoogle Scholar
  10. Hamilton, M. 2004. CENS: New directions in wireless embedded networked sensing of natural and agricultural ecosystems. In Proceedings of Converging Technologies for Agriculture and Environment (Sir Mark Oliphant Conference).Google ScholarGoogle Scholar
  11. Hellerstein, J., Hong, W., Madden, S., and Stanek, K. 2003. Beyond average: Towards sophisticated sensing with queries. In Information Processing in Sensor Networks. (IPSN'03). Vol. 1. Palo Alto, CA. Google ScholarGoogle Scholar
  12. Intanagonwiwat, C., Govindan, R., and Estrin, D. 2000. Directed diffusion: A scalable and robust communication paradigm for sensor networks. In Proceedings of the 6th Annual International Conference on Mobile Computing and Networking. ACM Press, New York, NY. 56--67. Google ScholarGoogle Scholar
  13. Kang, T. H., Rha, C., and Wallace, J. W. Seismic performance assessment of flat plate floor systems. CUREE-Kajima Joint Research Program.Google ScholarGoogle Scholar
  14. Karp, B. and Kung, H. T. 2000. GPSR: Greedy perimeter stateless routing for wireless networks. In Proceedings of Mobicom. Google ScholarGoogle Scholar
  15. Kohler, M. Availabel at http://www.cens.ucla.edu/portal/seismic_monitoring/.Google ScholarGoogle Scholar
  16. Kubiatowicz, J., Bindel, D., Chen, Y., Eaton, P., Geels, D., Gummadi, R., Rhea, S., Weatherspoon, H., Weimer, W., Wells, C., and Zhao, B. 2000. Oceanstore: An architecture for global-scale persistent storage. In Proceedings of ACM Architectural Support for Programming Languages and Operating Systems (ASPLOS'02). Google ScholarGoogle Scholar
  17. Li, X., Kim, Y.-J., Govindan, R., and Hong, W. 2003. Multi-dimensional range queries in sensor networks. In Proceedings of the 1st ACM Conference on Embedded Networked Sensor Systems (SenSys). Vol. 1. To appear. Google ScholarGoogle Scholar
  18. Mainwaring, A., Polastre, J., Szewczyk, R., Culler, D., and Anderson, J. 2002. Wireless sensor networks for habitat monitoring. In ACM International Workshop on Wireless Sensor Networks and Applications. Atlanta, GA. Google ScholarGoogle Scholar
  19. Meguerdichian, S., Koushanfar, F., Potkonjak, M., and Srivastava, M. 2001. Coverage problems in wireless ad-hoc sensor networks. In Proceedings of the IEEE Infocom.Google ScholarGoogle Scholar
  20. Rao, R. M. and Bopardikar, A. S. 1998. Wavelet Transforms: Introduction to Theory and Applications. Addison Wesley.Google ScholarGoogle Scholar
  21. Ratnasamy, S., Francis, P., Handley, M., Karp, R., and Shenker, S. 2001. A scalable content addressable network. In Proceedings of the 2001 ACM SIGCOMM Conference. Google ScholarGoogle Scholar
  22. Ratnasamy, S., Karp, B., Yin, L., Yu, F., Estrin, D., Govindan, R., and Shenker, S. 2002. Ght---a geographic hash-table for data-centric storage. In the 1st ACM International Workshop on Wireless Sensor Networks and Their Applications. Google ScholarGoogle Scholar
  23. Rowston, A. and Druschel, P. 2001. Storage management and caching in past, a large-scale, persistent peer-to-peer storage utility. In 18th ACM Symposium on Operating Systems Principles. Vol. 1. Lake Louise, Canada. Google ScholarGoogle Scholar
  24. Vetterli, M. and Kovacevic, J. 1995. Wavelets and Subband coding. Prentice Hall. Google ScholarGoogle Scholar
  25. Vitter, J. S., Wang, M., and Iyer, B. 1998. Data cube approximation and histograms via wavelets. In Proceedings of the Conference on Information and Knowledge Management (CIKM'98). Washington D.C., 69--84. Google ScholarGoogle Scholar
  26. Wang, W., Yang, J., and Muntz, R. 1997. Sting: A statistical information grid approach to spatial data mining. In Proceedings of the 23rd Very Large Data Base Conference. Vol. 1. Athens, Greece. Google ScholarGoogle Scholar
  27. Widmann, M. and Bretherton, C. 50 km resolution daily preciptation for the Pacific Northwest, 1949--94. Available at http://tao.atmos.washington.edu/data_sets/widmann/.Google ScholarGoogle Scholar
  28. Xu, N., Rangawala, S., Chintalapudi, K., Ganesan, D., Broad, A., Govindan, R., and Estrin, D. 2004. A wireless sensor network for structural monitoring. In Proceedings of the 2nd ACM Conference on Embedded Networked Sensor Systems (SenSys). Google ScholarGoogle Scholar
  29. Xu, Y., Heidemann, J., and Estrin, D. 2001. Geography-informed energy conservation for ad hoc routing. In Proceedings of the ACM/IEEE International Conference on Mobile Computing and Networking (Mobicom). Rome, Italy, 70--84. Google ScholarGoogle Scholar
  30. Zhao, Y., Govindan, R., and Estrin, D. 2002. Residual energy scans for monitoring wireless sensor networks. In Proceedings of the IEEE Wireless Communications and Networking Conference.Google ScholarGoogle Scholar

Index Terms

  1. Multiresolution storage and search in sensor networks

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in

            Full Access

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader