Data Uploading in Hybrid V2V/V2I Vehicular Networks: Modeling and Cooperative Strategy
Bibliographic record
Abstract
Supporting data dissemination in vehicular networks is difficult due to high mobility of vehicle traffic, limited communication resources, and dynamic communication requirements. In this paper, we investigate data dissemination in hybrid vehicular networks, where messages generated at vehicles should be uploaded to the roadside unit (RSU) assisted by vehicle-to-vehicle (V2V) communications, using vehicles traveling in both directions as relays. The objective is to optimize the resource utilization and reduce the data delivery delay. We first analyze the data uploading capacity and delivery delay in hybrid vehicular networks with the store-carry-and-forward mechanism. Applying the analytical results and given the traffic and data information, a distributed multisource scheduling algorithm is proposed. Extensive simulations are conducted to verify the correctness of the analysis. It shows that the proposed algorithm significantly improves the data dissemination efficiency compared with the existing solutions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".