On the End-to-End Delay in a One-Way VANET
Bibliographic record
Abstract
There has been much increased interest in the academic and industrial research communities on vehicular ad-hoc networks (VANETs). In this paper, we present an analytical model to study the end-to-end delay in a one-way VANET. This paper proposes an analytical formula for the end-to-end delay probability distribution. Using the derived probability distribution, the probability that the end-to-end delay is lower than a given threshold may be calculated. In addition, one can straightforwardly study the impact of parameters such as wireless communication range, vehicular densities, distance between source the destination, and minimum and maximum vehicle speeds on the end-to-end delay. This can help to better understand data dissemination in VANETs. Moreover, closed forms for lower and upper bounds on the end-to-end delay probability distribution are obtained. Extensive simulation results demonstrate the accuracy of our analysis.
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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 | 0.001 | 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) | 0.001 | 0.002 |
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".