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.
- 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 Scholar
- Cerpa, A. and Estrin, D. 2002. Ascent: Adaptive self-configuring sEnsor networks topologies. In Proceedings of the IEEE Infocom. New York, NY.Google Scholar
- Chakrabarti, K., Garofalakis, M., Rastogi, R., and Shim, K. 2001. Approximate query processing using wavelets. VLDB J. 10, 2--3, 199--223. Google Scholar
- Davis, G. Wavelet Image Compression Kit.Google Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- Kang, T. H., Rha, C., and Wallace, J. W. Seismic performance assessment of flat plate floor systems. CUREE-Kajima Joint Research Program.Google Scholar
- Karp, B. and Kung, H. T. 2000. GPSR: Greedy perimeter stateless routing for wireless networks. In Proceedings of Mobicom. Google Scholar
- Kohler, M. Availabel at http://www.cens.ucla.edu/portal/seismic_monitoring/.Google Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- Rao, R. M. and Bopardikar, A. S. 1998. Wavelet Transforms: Introduction to Theory and Applications. Addison Wesley.Google Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- Vetterli, M. and Kovacevic, J. 1995. Wavelets and Subband coding. Prentice Hall. Google Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
Index Terms
- Multiresolution storage and search in sensor networks
Recommendations
Optimize Storage Placement in Sensor Networks
Data storage has become an important issue in sensor networks as a large amount of collected data need to be archived for future information retrieval. Storage nodes are introduced in this paper to store the data collected from the sensors in their ...
Data storage placement in sensor networks
MobiHoc '06: Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computingData storage has become a important issue in sensor networks as a large amount of collected data need to be archived for future information retrieval. This paper introduces storage nodes to store the data collected from the sensors in their proximities. ...
A power-saving data storage scheme for wireless sensor networks
In wireless sensor network (WSN), sensors are small, inexpensive, and computable. However, they are limited in power, memory, and computational capacities. A large number of tiny sensors are usually deployed randomly to monitor one or more phenomena to ...
Comments