Data-Driven Processing in Sensor Networks
Summary: Data-driven processing: nodes use local models and in-network suppression to avoid redundant radio reporting, with occasional checks against measurements to preserve correctness. Describes suppression-scheme design, model integration, and fault-tolerance and layering techniques for energy-efficient, scalable spatio-temporal sensing. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Adam Silberstein
- 2. Rebecca Braynard
- 3. Gregory Filpus
- 4. Gavino Puggioni
- 5. Alan Gelfand
- 6. Kamesh Munagala
- 7. Jun Yang
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 468 | MauveDB: Supporting Model-based User Views in Database Systems | 2006 | SIGMOD | 0.00022407392 |
| 477 | Model-Driven Data Acquisition in Sensor Networks | 2004 | VLDB | 0.00022205608 |
| 1,069 | Adaptive Stream Resource Management Using Kalman Filters | 2004 | SIGMOD | 0.00014285617 |
| 2,120 | Using Probabilistic Models for Data Management in Acquisitional Environments | 2005 | CIDR | 9.5017579e-05 |
| 3,127 | Compressing Historical Information in Sensor Networks | 2004 | SIGMOD | 7.5200903e-05 |
| 5,500 | Energy-Efficient Monitoring of Extreme Values in Sensor Networks | 2006 | SIGMOD | 5.4733109e-05 |
| 7,995 | Constraint Chaining: On Energy-Efficient Continuous Monitoring in Sensor Networks | 2006 | SIGMOD | 4.6072096e-05 |
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