High Speed Multi-Hop Data Dissemination for Heterogeneous Transmission Ranges in VANETs
Bibliographic record
Abstract
In multi-hop dissemination a timer-based and a probabilistic methods are the high performing methods in Vehicular Ad-hoc Networks (VANETs), due to simplicity, low overhead, and high efficiency. However, such approaches neglect the fact that vehicles transmission ranges are typically heterogeneous due to different transmission power values. In our previous work, we introduce new area-based methods for heterogeneous transmission ranges in vehicle networks, i.e. Area Defer Transmission (ADT), Area-based Probabilistic Transmission (APT), and an integration of area-based timer and probabilistic transmission methods. ADT proves it efficiency over distance-based methods, and the probabilistic version due to high delivery ratio, high propagation speed and reachability. The work is extended and we introduce an enhancement of ADT defer time formula. The enhancement gives vehicles with larger potential coverage area high opportunity to relay the message than the one with small potential coverage area or small communication range. The performance of the proposed enhancement is compared to ADT and evaluated using an actual road map with complex road scenarios and real movement traces. The simulation results show that the dissemination speed improves dramatically, with better saved-retransmission 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".