Unmanned Aerial Vehicles as Store-Carry-Forward Nodes for Vehicular Networks
Bibliographic record
Abstract
A fully connected vehicular ad hoc network (VANET) establishes a strong foundation for the development of smart cities, where one of the main objectives is the improvement of the welfare of commuting passengers. The availability of a multi-hop path across a VANET system, through vehicle-to-vehicle communication, depends mainly on the vehicular density and the willingness of vehicles to cooperate with one another. This paper proposes to minimize the path availability's dependence on vehicular density and cooperation, by utilizing unmanned aerial vehicles (UAVs). Particularly, this paper explores, both mathematically as well as through an extensive simulation study, the advantages of exploiting UAVs as store-carry-forward nodes so as to enhance the availability of a connectivity path as well as to reduce the end-to-end packet delivery delay. The obtained results shed clear light on the benefits emanating from the coupling of UAVs with vehicles in the context of a highly promising, innovative, and hybrid vehicular networking architecture.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".