A Performance Modeling of Vehicular Ad Hoc Networks (VANETs)
Bibliographic record
Abstract
In this paper, we study the statistical properties of the connectivity of vehicular ad hoc networks (VANETs) with user mobility at the steady state. It is assumed that the nodes travel along a single dimensional strip with finite length. The nodes arrive at the service strip through one of the traffic entry points following a Poisson process and move along the strip in the same direction according to a user mobility model until they reach their exit point. The service strip allows the users with different speeds to pass each other. The nodes, which are within the service strip, are able to participate in communications. We derive the probability distribution of the user population size within the service strip and node's location distribution. Then, we determine the mean cluster size, fraction of nodes within the cluster and probability that nodes will form a single cluster. This work shows significance of mobility on the connectivity of ad hoc networks. The results of the paper may be used to avoid traffic congestion in the highways.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".