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Multiresolution storage and search in sensor networks

Deepak Ganesan, Ben Greenstein, Deborah Estrin, John heidemann, and Ramesh Govindan
USC/Information Sciences Institute

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.

Availability

This paper is available in several formats: abstract web page with pointers and cites, PDF, paper copies can be obtained by mail to the authors. Copyright terms for this paper appear below.

Reference

Ganesan05a
Deepak Ganesan, Ben Greenstein, Deborah Estrin, John heidemann, and Ramesh Govindan. Multiresolution storage and search in sensor networks. ACM Transactions on Storage, 1 (3 ), pp. 277-315, August, 2005. <http://www.isi.edu/~johnh/PAPERS/Ganesan05a.html>. 
@article{Ganesan05a,
	author = "Deepak Ganesan and Ben Greenstein and Deborah
                         Estrin and John heidemann and Ramesh Govindan",
	title = "Multiresolution storage and search in sensor networks",
	journal = "{ACM} Transactions on Storage",
	year = "2005",
	volume = "1",
	number = "3",
	month = "August",
	pages = "277--315",
	keywords = "dimensions, multiresolution storage, sensor
                         network storage",
	url = "http://www.isi.edu/~johnh/PAPERS/Ganesan05a.html",
	pdfurl = "http://www.isi.edu/~johnh/PAPERS/Ganesan05a.pdf",
	doi = "http://doi.acm.org/10.1145/1084779.1084780",
	myorganization = "USC/Information Sciences Institute",
	copyrightholder = "{ACM}",
}

Copyright

This paper is copyright © 2005 by ACM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.

© 2006 $Date$