Throughput evaluation for cooperative drive-thru Internet using microscopic mobility model
Bibliographic record
Abstract
The recent advances in wireless communication techniques have made possible for vehicles to download from the roadside communications infrastructure, namely drive-thru Internet. However, due to the fast-motions, harsh and intermittent wireless channels, the download volume of individual vehicles per drive-thru is quite limited as observed in real-world tests. This severely restricts the service quality of upper-layer applications, such as file download and video streaming. To address this issue, we take a historical approach by evaluating the integrated download throughput of a cooperative vehicle group in the highway environment. In specific, we first introduce a practical microscopic vehicular mobility model, which takes the randomness of speed update and safety distance requirement into account. Then, we analyze and formulate the number of contending vehicles within the coverage range of access point (AP) in the single-lane highways scenario, which can also be easily extended into the multi-lane highways scenario. Furthermore, we derive the data download volume by a vehicle per drive-thru, and analyze the relationship between the mobility speed and the data download volume. Finally, we derive the number of cooperative vehicles required for completing a download task in our investigated highways drive-thru Internet. The analytical model and evaluation results provide general guidance for cooperative content distribution and protocol design in drive-thru Internet.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".