Performance evaluation of video dissemination protocols over Vehicular Networks
Bibliographic record
Abstract
There are several outstanding services envisioned for Vehicular Networks that require the provision of video dissemination support. These services range from enhancing safety via the dissemination of video from an accident scene to advertisements of local services or events. This work considers the infrastructureless scenario of Vehicular Ad Hoc Networks (VANETS). The dissemination of video content over VANETs is extremely challenging mainly due to the network's dynamic topology and stringent requirements of video streaming. This paper studies the main approaches aimed towards an effective and efficient solution for video dissemination over VANETs. Furthermore, some of these solutions have been selected to discuss their techniques and suitability for video dissemination and compare their performance. This work describes in detail the process of video dissemination over VANETs and presents a thorough evaluation of existing solutions. This permitted us to summarize our observations and indicate the direction for the design of new 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 | 0.001 | 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 | 0.000 |
| 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".