Timer-based Decision in Speed-Variant Data Forwarding for VANETs
Bibliographic record
Abstract
Vehicular Clouds heavily rely on the underlying Vehicular Networks to discover, announce, and exchange services and resources. The assembly and control of such clouds depend upon reliable and efficient data dissemination among vehicles and roadside units. Dissemination allows for critical information to be spread efficiently and widely through the VANETs. It is, therefore, imperative to create a dissemination algorithm that reduces the number of redundant messages. The biggest concern is to define an effective and efficient data dissemination algorithm that can be used in critical areas in traffic like intersections. Several issues must be considered in dense vehicular regions, such as broadcast storms and redundancy. Several approaches have already shown a reduction in the redundancy and overhead. Still, they may need to improve on the variance of a dynamically changing topology and exponential growth rate of messages sent. These approaches include a speed adaptive probabilistic dissemination, which is a lightweight approach. This work focuses on controlling the dissemination pace by applying timers and limiting forwarding messages on the speed adaptive broadcast algorithm to reduce overhead and redundancy while ensuring the broadcast is transmitted evenly. The timers are useful in high-density traffic and conclusively showed a broadcast overhead reduction.
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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 | 0.011 |
| 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 | 0.001 |
| 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".