Performance Evaluation of VeMAC Supporting Safety Applications in Vehicular Networks
Bibliographic record
Abstract
Vehicular ad hoc networking (VANET) is an emerging paradigm that is expected to increase the public safety standards and enhance the safety level of drivers/passengers and pedestrians on roads through a variety of applications. We have recently proposed VeMAC, a medium access control protocol that supports a reliable one-hop broadcast service necessary for high priority safety applications in VANETs. This paper explains how the VeMAC protocol can deliver both periodic and event-driven safety messages in vehicular networks and presents a detailed delivery delay analysis, including queueing and service delays, for both types of safety messages. The probability mass function of the service delay is first derived; then, the D/G/1 and M/G/1 queueing systems are used to calculate the average queueing delay of the periodic and event-driven safety messages, respectively. In addition, a comparison between the VeMAC protocol and IEEE 802.11p standard is presented via extensive simulations using the network simulator ns-2 and the microscopic vehicle traffic simulator VISSIM. A real city scenario is considered and different performance metrics are evaluated, including the network goodput, protocol overhead, channel utilization, protocol fairness, probability of a transmission collision, and message delivery delay.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".