A Performance Modeling of Connectivity in Vehicular <i>Ad Hoc</i> Networks
Bibliographic record
Abstract
In this paper, we study the statistical properties of the connectivity of VehicularAd hocNETworks (VANETs) with user mobility. It is assumed that the nodes travel along a multilane highway that allows vehicles to pass each other. The nodes arrive at the highway through one of the traffic entry points according to a Poisson process and then travel in the same direction according to a user mobility model until they reach their exit points. The nodes on the highway may be able to communicate with each other. We derive the probability distribution of the node population size on the highway and the node's location distribution. Then, we determine the mean cluster size and the probability that the nodes will form a single cluster. The analysis of this paper also applies to any path in a network of highways, as well as to two-way traffic. The numerical results show the significance of mobility on the connectivity of VANETs. We also present simulation results that confirm the accuracy of the analysis. The results of this paper may be used to study the routing algorithms, throughput, or delay in VANETs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".