UAV-Aided Data Delivery Scheme Based on Opportunistic Virtual Intersections for Smart Transportation Networks
Bibliographic record
Abstract
Smart transportation networks, featured by high mobility and intermittent connectivity, are facing critical challenges in data delivery especially when the network is sparse and with less or no support from any infrastructure, such as in disaster and military environments. Unmanned aerial vehicles (UAVs), due to their flexible mobility, can be envisioned to enhance the connectivity of ground vehicular networks. In this paper, incorporating UAVs into vehicular networks, a novel data delivery scheme, named UAV-assisted data delivery (UADD), is proposed for smart transportation networks. We introduce a novel concept called “opportunistic virtual intersections”, at which UAVs can communicate with ground vehicles, and assist to deliver data packets. Specifically, at an opportunistic virtual intersection, UAVs may act as a relay, which can relay data packets between two vehicles as an intermediate hop, or deliver data packets in a store-carry-and-forward manner. The data forwarding rules are devised based on expected delivery delay and delivery probability of data packets. The performance evaluation demonstrates that the proposed UAV-assisted data delivery scheme can achieve a significant improvement of data delivery performance in terms of delivery delay and delivery ratio.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".