Delay analysis for drone-based vehicular Ad-Hoc Networks
Bibliographic record
Abstract
Using Unmanned Aerial Vehicles (UAVs) or drones in Vehicular Ad-hoc Networks (VANETs) has started to attract attention. This paper proposes a mathematical framework to determine the minimum drone density (maximum separation distance between two adjacent drones) that stochastically limits the worst case for the vehicle-to-drone packet delivery delay. In addition, it proposes a drones-active service (DAS) that is added to the location service in a VANET to obtain the required number of active drones based on the current vehicular density while satisfying a probabilistic requirement for vehicle-to-drone packet delivery delay. Our goal is boosting VANET communications using infrastructure drones to achieve the minimum vehicle-to-drone packet delivery delay. We are interested in two-way highway VANET networks with low vehicular density. The simulation results show the accuracy of our mathematical framework and reflect the relation between the vehicle-to-drone packet delivery delay and the drone density.
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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 | 0.001 |
| 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) | 0.002 | 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".