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)
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Authors
- 1. Liu Yu
- 2. Jianzhong Li
- 3. Hong Gao
- 4. Xiaolin Fang
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Showing 6 of 6 cited papers.
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 | 0.0010813443 |
| 416 | Approximate Query Processing Using Wavelets | 2000 | VLDB | 0.00023773968 |
| 477 | Model-Driven Data Acquisition in Sensor Networks | 2004 | VLDB | 0.00022205608 |
| 2,635 | Online Outlier Detection in Sensor Data Using Non-Parametric Models | 2006 | VLDB | 8.4081184e-05 |
| 3,127 | Compressing Historical Information in Sensor Networks | 2004 | SIGMOD | 7.5200903e-05 |
| 7,353 | GAMPS: Compressing Multi Sensor Data by Grouping and Amplitude Scaling | 2009 | SIGMOD | 4.7484027e-05 |
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