A Reliable and Efficient Time Synchronization Protocol for Heterogeneous Wireless Sensor Network
Bibliographic record
Abstract
L-SYNC is a synchronization protocol for Wireless Sensor Networks which is based on larger degree clustering providing efficiency in homogeneous topologies. In L-SYNC, the effectiveness of the routing algorithm for the synchronization precision of two remote nodes was considered. Clustering in L-SYNC is according to larger degree techniques. These techniques reduce cluster overlapping, resulting in the routing algorithm requiring fewer hops to move from one cluster to another remote cluster. Even though L-SYNC offers higher precision compared to other algorithms, it does not support heterogeneous topologies and its synchronization algorithm can be influenced by unreliable data. In this paper, we present the L-SYNCng (L-SYNC next generation) protocol, working in heterogeneous topologies. Our proposed protocol is scalable in unreliable and noisy environments. Simulation results illustrate that L-SYNCng has better precision in synchronization and scalability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".