MUDDS: Multi-metric Unicast Data Dissemination Scheme for 802.11p VANETs
Bibliographic record
Abstract
Vehicular ad hoc networks (VANETs) leverage communication equipment and infrastructures to improve road safety. These networks, by the rapid change of their topology, can experience mainly two major problems; (1) the broadcasting storm and (2) the network disconnection due respectively to high vehicles density and their velocity. In this paper, we propose a new unicast data dissemination scheme based on distances estimation using Received Signal Strength (RSS) measurements and congestion detection by mean of a newly designed metric; called Multi-metric Unicast Data Dissemination Scheme (MUDDS). MUDDS adapts the transmission range so that congestion can be avoided. It performs the best available link choice to guarantee both reliable transmission and minimum delivery delay. MUDDS focuses on the broadcasting storm and the network disconnection problems simultaneously. Simulation results confirm the effectiveness of the proposed on-demand adaptation and relaying scheme and its impact on network performance under various traffic constraints.
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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 | 0.003 |
| 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 | 0.000 |
| 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".