Performance Modeling of Safety Messages Broadcast in Vehicular Ad Hoc Networks
Bibliographic record
Abstract
In vehicular ad hoc networks (VANETs), because all vehicles in range are shown as destination nodes and less time is spent for the medium access process, broadcast communication is considered a highly appropriate technique for the dissemination of safety messages in such networks. However, the lack of request-to-send/clear-to-send handshaking and packet acknowledgment makes the communication more vulnerable to interferences, thus resulting in lower communication reliability. In this paper, we present an analytical model for the performance evaluation of safety message dissemination in vehicular ad hoc networks with two priority classes. In particular, considering the IEEE 802.11 broadcast protocol and using 2-D Markov modeling, we derive the joint distribution of the numbers of low-priority periodic messages, which are in transmission mode and in a backoff process in a highway. Then, the result is used to derive the average dissemination delay of high-priority event-driven messages in the presence of the low-priority traffic in the network. The results are helpful in determining a good tradeoff between network parameters such as vehicles' transmission range, safety traffic generation rate, and medium access control (MAC) parameters to satisfy the required delay bounds for the critical high-priority traffic.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".