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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)

Paper ID
56
Venue
CIDR
Year
2007
Pagerank
5.093636e-05
Overall Rank
12,614 | 13.46%
DOI
-

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BibTeX Citation

@inproceedings{silberstein_cidr07,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '07},
        title = {{Data-Driven Processing in Sensor Networks}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Silberstein, Adam and Braynard, Rebecca and Filpus, Gregory and Puggioni, Gavino and Gelfand, Alan and Munagala, Kamesh and Yang, Jun},
        year = {2007}
}

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