On the Analytical Calculation of the Probability Distribution of End-to-End Delay in a Two-Way Highway VANET
Bibliographic record
Abstract
In several papers, analytical calculations of the mean value of the end-to-end delay in highway vehicular ad-hoc networks (VANETs) have been presented. Unfortunately, none of these papers presented calculations of the probability distribution of this delay, which is necessary to give probabilistically guaranteed upper bounds on the end-to-end delay in such VANETs. In a previous paper, we introduced the first analytical framework for the calculation of the probability distribution, and not only the mean, of the end-to-end delay in multi-lane one-way highway VANETs. This made it possible to provide guarantees of transmission in a given time frame with known confidence. In this paper, that previous work is extended to two-way multi-lane highways by taking into consideration vehicles travelling in both directions. The probability distribution of the end-to-end delay is calculated herein and its dependence on system parameters, such as speed distributions in the two directions, communication range, and vehicle densities, are analyzed. Computer simulations are used to verify the analytical model. The good agreement between simulation results and the analytical calculations demonstrates the correctness and accuracy of the proposed analytical model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".