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Energy-efficient data organization and query processing in sensor networks
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Volume 2 ,  Issue 1  (January 2005) table of contents
Special issue: Best of sensys 2004 work-in-progress
Pages: 7 - 12  
Year of Publication: 2005
Authors
Ramakrishna Gummadi  Computer Science Department, University of Southern California, Los Angeles, CA
Xin Li  Computer Science Department, University of Southern California, Los Angeles, CA
Ramesh Govindan  Computer Science Department, University of Southern California, Los Angeles, CA
Cyrus Shahabi  Computer Science Department, University of Southern California, Los Angeles, CA
Wei Hong  Intel Research at Berkeley, Berkeley, CA
Publisher
ACM  New York, NY, USA
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ABSTRACT

Recent sensor networks research has produced a class of data storage and query processing techniques called Data-Centric Storage that leverages locality-preserving distributed indexes to efficiently answer multi-dimensional range and range-aggregate queries. These distributed indexes offer a rich design space of a) logical decompositions of sensor relation schema into indexes, as well as b) physical mappings of these indexes onto sensors. In this paper, we discuss this space for energy-efficient data organizations (logical and physical mappings of tuples and attributes to sensor nodes) and examine the performance of purely local query optimization techniques for processing queries that span such decomposed relations.-


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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{2} Crossbow Technology, Inc. MTS Data Sheet. http://www.xbow.com/Products/productsdetails.aspx?sid=72.
 
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{3} D. Ganesan, D. Estrin, and J. Heidemann. DIMENSIONS: Why do we need a new Data Handling architecture for Sensor Networks? In Proc. HotNets-I, Princeton, NJ, October 2002.
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{5} B. Greenstein, D. Estrin, R. Govindan, S. Ratnasamy, and S. Shenker. DIFS: A Distributed Index for Features in Sensor Networks. In Proc. IEEE WSNPA, Anchorage, AK, May 2003.
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Collaborative Colleagues:
Ramakrishna Gummadi: colleagues
Xin Li: colleagues
Ramesh Govindan: colleagues
Cyrus Shahabi: colleagues
Wei Hong: colleagues