Data Sweeper: A Proactive Filtering Framework for Error-Bounded Sensor Data Collection
Bibliographic record
Abstract
This paper presents data sweeper-a novel framework that attempts to reduce network traffic for error-bounded data collection in wireless sensor networks. Unlike existing passive filters, a data sweeper migrates in the network and proactively suppresses data updates while maintaining the user-defined error bound. Intuitively, the migration of a data sweeper learns the data change of each sensor node on the fly, which helps to maximize the filtering capacity. We design the data sweeper framework in such a way that it can accommodate diverse query specifications and be easily incorporated into the existing sensor network protocols. Moreover, we develop efficient strategies for query precision maintenance, sweeper migration, and data suppression within the framework. In particular, in order to maximize traffic reduction and adapt to online data updates, a Lagrangian relaxation-based algorithm is proposed for data suppression. Extensive simulations based on real-world traces show that the data sweeper significantly reduces the network traffic and extends the system lifetime under various network configurations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".