| Energy-efficient data organization and query processing in sensor networks |
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ACM SIGBED Review
archive
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
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Authors
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Ramakrishna Gummadi
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Computer Science Department, University of Southern California, Los Angeles, CA
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Xin Li
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Computer Science Department, University of Southern California, Los Angeles, CA
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Ramesh Govindan
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Computer Science Department, University of Southern California, Los Angeles, CA
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Cyrus Shahabi
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Computer Science Department, University of Southern California, Los Angeles, CA
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Wei Hong
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Intel Research at Berkeley, Berkeley, CA
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Downloads (6 Weeks): 1, Downloads (12 Months): 15, Citation Count: 1
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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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