Analysis of broadcasting delays in vehicular <i>ad hoc</i> networks
Bibliographic record
Abstract
Abstract High mobility of nodes in vehicular ad hoc networks (VANETs) may lead to frequent breakdowns of established routes in conventional routing algorithms commonly used in mobile ad hoc networks. To satisfy the high reliability and low delivery‐latency requirements for safety applications in VANETs, broadcasting becomes an essential operation for route establishment and repair. However, high node mobility causes constantly changing traffic and topology, which creates great challenges for broadcasting. Therefore, there is much interest in better understanding the properties of broadcasting in VANETs. In this paper we perform stochastic analysis of broadcasting delays in VANETs under three typical scenarios: freeway, sparse traffic and dense traffic, and utilize them to analyze the broadcasting delays in these scenarios. In the freeway scenario, the analytical equation of the expected delay in one connected group is given based on statistical analysis of real traffic data collected on freeways. In the sparse traffic scenario, the broadcasting delay in an n‐vehicle network is calculated by a finite Markov chain. In the dense traffic scenario, the collision problem is analyzed by different radio propagation models. The correctness of these theoretical analyses is confirmed by simulations. These results are useful to provide theoretical insights into the broadcasting delays in VANETs. Copyright © 2010 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".