RAPS: a precision‐adaptive protocol towards improved data fidelity in wireless sensor networks
Bibliographic record
Abstract
ABSTRACT Achieving high data quality and efficient network resource utilization is two major design objectives of wireless sensor networks (WSNs). However, these two objectives are often conflictive. By allowing sensors to report sampled data at high rates, fine‐grained data quality can be obtained. However, the limited resources of a WSN make it difficult to support very high traffic rate. Therefore, the capability of adaptively adjusting sensor nodes' traffic‐generating rates on the basis of the availability of network resources and application requirements is critical. This issue has attracted much attention recently, and some work has been carried out. To achieve high data quality and improved utilization of network resources, in this paper, we propose rate‐based adaptive precision setting (RAPS) protocol, which works in a way that each sensor can adaptively adjust its traffic‐generating rate on the basis of the current network resources availability and application requirements. RAPS introduces the following two key factors into its design: application's precision requirement and packet arrival rate. Analytical and simulation results show that RAPS can achieve improved data quality while reducing packet delivery latency. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".