Data dissemination for heterogeneous transmission ranges in VANets
Bibliographic record
Abstract
Distance is a key measure when implementing timer-based dissemination protocols in Vehicular Ad-hoc Networks (VANets). In which the transmission is deferred proportional to distance, aiming to order vehicles transmission, such that the farthest vehicle gets the highest opportunity to relay the message. This will ensure long hops along the road to speed up the dissemination and cover more nodes. However, in case of heterogeneous transmission ranges, the farthest distance will not ensure a proper choice of relay nodes to disseminate the message. Vehicles, whose transmission area enclosed by the sender or have small non-covered transmission area, might be chosen as relay vehicle. This may inhabit other nodes from relaying the message and end the dissemination process early and before it reach the required region. In this paper, we propose the Area Defer Transmission (ADT) dissemination algorithm. ADT enables each vehicle to independently decide to transmit or suppress transmission considering heterogeneous transmission ranges and the amount of area that would be covered by potential new transmission. The performance of the proposed ADT algorithm has been evaluated using an actual road map with complex road scenarios and real movement traces. It has also been thoroughly investigated and compared with other distance-based algorithms. The results demonstrate that ADT achieves high delivery ratio, high propagation speed and less relay ratio with fewer hops that reach long distances.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".