Probabilistic Area-Based Dissemination for Heterogeneous Transmission Ranges in Vehicular Ad-Hoc Networks
Bibliographic record
Abstract
In multi-hop dissemination, timer-based and probabilistic methods are high performing methods in Vehicular Ad-hoc Networks (VANETs), owing to their simplicity, low overhead and high efficiency. However, such approaches neglect the fact that vehicle transmission ranges are typically heterogeneous, which could be due to dynamic adjustment of the transmission power value. In our previous work, we introduced a new timer-based method that considers heterogeneous transmission power: area defer transmission (ADT). ADT proves its efficiency over distance-based versions by its high delivery ratio, high propagation speed and reachability. We have extended this work to introduce a probabilistic approach to reduce unnecessary redundancy in heterogeneous environments. We have also examined the integration of the timer area-based and probabilistic area-based methods. Moreover, we have thoroughly investigated the influence of the parameter Tmax in the defer time formula on both approaches. We evaluated the performance of the proposed methods using an actual road map with complex road scenarios and real movement traces. The simulation results show that the probabilistic area-based approach is very fast at disseminating data. It also achieves a reduction in redundancy, but at the expense of reaching more vehicles and long distances. Redundancy and retransmission are reduced more with controlling defer time parameters in timer area-based approaches, without compromising reachability in the network and the same delivery ratio.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".