A collision-based beacon rate adaptation scheme(CBA) for VANETs
Bibliographic record
Abstract
Safety applications in VANET use two types of messages (a)periodical messages/beacons: they are broadcast several times per second to exchange information with neighbors; and (b) warning (event driven) messages: they are generated when an event occurs (e.g., a car accident) and are disseminated in the network to notify nodes of interest. Although warning messages have higher priority, beacons are equally as important since a good dissemination strategy usually relies on information provided by beacons to choose forwarding nodes. However, in dense networks, beacons may cause network congestion leading to performance degradation of safety applications. In this paper, we propose CBA: a congestion control approach that uses the number of detected collisions as a metric to control the beacon generation frequency and therefore reduce the effect of congestion. Simulation results show that our proposed scheme achieves a balanced trade-off between beacon information accuracy and beacon related overhead.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".