Adaptive Time Synchronization for Wireless Sensor Networks with Self-Calibration
Bibliographic record
Abstract
Time synchronization is important for wireless sensor networks because it facilitates cooperation among nodes and helps raise power efficiency. Time synchronization protocols like TPSN, RBS and FTSP have provided great schemes to fulfill fast synchronization with efficiency. In some applications, nodes might hope to sleep for a long time without timestamp exchanges with other nodes. In that case, accurate time drift prediction is quite necessary. For that purpose, firstly, we propose a time synchronization scheme, which fully utilizes the broadcast nature. The scheme achieves time synchronization with fewer timestamps compared with RBS and TPSN. Secondly, we introduce a method to find relative time drift rate on the fly. Thirdly, we introduce a scheme to predict time drift rates of the next few hours. We also analyze a few factors that deteriorate frequency drift or time drift rate. The diurnal periodical environment trend, instead of mathematical extrapolation, is used for time drift rates prediction of the next few hours.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".