Research on Time Synchronization Algorithm Based on Dynamic Clustering in Wireless Sensor Networks
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Bibliographic record
Abstract
This paper proposed a dynamic clustering time synchronization algorithm.First of all,for the characteristics of wireless networks ranging which the data in the network is not too much and the cluster head node not need to fusion the data in the network,it improves the LEACH algorithm,proposes a GLEACH algorithm and divides the whole network into different cluster using GLEACH algorithm.Take the base station and the cluster head node as reference nodes and use the Two-way synchronization mechanism similar to that of TPSN algorithm,step by step,to achieve full network time synchronization.Meanwhile it combines the Dynamic Clustering Algorithm,to balance the consumption of the whole network power and overcome the overload of TPSN reference nodes,resulting in premature death of certain nodes.Finally,the results show that this coordinated algorithm can prolong the lifetime of network and improve the synchronization accuracy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it