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Enabling epsilon-Approximate Querying in Sensor Networks

Summary: Proposes epsilon-approximate querying (EAQ) as a uniform data access scheme for sensor networks, enabling incremental refinement to any target accuracy and energy-aware processing. A novel shuffling method converts data into multi-version arrays (MVA); from prefixes we recover approximate full data with per-item error bounds, supporting spatial window, value-range, and QoS queries, validated on a real testbed. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9938
Venue
VLDB
Year
2009
Pagerank
4.1945683e-05
Overall Rank
12,342 | 14.14%
DOI
-

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
14 Online Aggregation 1997 SIGMOD 0.0010801504
405 Approximate Query Processing Using Wavelets 2000 VLDB 0.00024057494
477 Model-Driven Data Acquisition in Sensor Networks 2004 VLDB 0.00022221803
2,629 Online Outlier Detection in Sensor Data Using Non-Parametric Models 2006 VLDB 8.4160309e-05
3,121 Compressing Historical Information in Sensor Networks 2004 SIGMOD 7.5271941e-05
7,356 GAMPS: Compressing Multi Sensor Data by Grouping and Amplitude Scaling 2009 SIGMOD 4.7529612e-05
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