A Performance Modeling of Connectivity in Vehicular <i>Ad Hoc</i> Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this paper, we study the statistical properties of the connectivity of Vehicular <emphasis emphasistype="italic">Ad hoc</emphasis> NETworks (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. </para>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it