Network partition-aware geographical data dissemination
Bibliographic record
Abstract
Vehicular Ad hoc Networks (VANETs) have attracted the attention of the research community recently as they have opened up a myriad of on the road applications and increased their potential by providing accident-free and intelligent transport systems. The envisaged applications, as well as some inherent VANET characteristics make data dissemination an essential service and a challenging task in these networks. The existing solutions for data dissemination do not effectively address broadcast storm and network partition problems when considered together. To tackle these problems, we propose a novel GEographical Data Dissemination of Alert Information and Aware of Network Partition (GEDDAI-NP), which eliminates the broadcast storm and maximizes data dissemination capabilities across network partitions with short delays and low overhead. The simulation results show that the data dissemination performed by GEDDAI-NP provides better efficiency than other algorithms, outperforming them in different scenarios in all the evaluations carried out.
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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) | 0.002 | 0.001 |
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".