Adaptive Clock Skew Estimation with Interactive Multi-Model Kalman Filters for Sensor Networks
Bibliographic record
Abstract
Clock synchronization is a fundamental issue in communication networks and distributed systems, and clock skew is the inherent cause for clock desynchronization. Clock skew estimation is essential to improve the efficiency and reduce the overhead of clock synchronization schemes, and it is especially beneficial for resource-constrained devices such as sensor nodes in dynamic environments. According to the measurement, clock skew is environment sensitive, and no existing clock skew estimation schemes can accurately capture such dynamic behaviors. In this paper, we investigate a general clock synchronization problem with variable clock skews and propose a new skew estimation model based on a hybrid approach to characterizing the dynamic of clock skews. To estimate the time-varying clock state vector, we employ the Interactive Multi-Model (IMM) Kalman filter, which can make soft decisions by combining results from different models. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed adaptive clock skew estimation algorithm, which achieves a better performance with moderate computational complexity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".