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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".