Impact of Mobility on VANETs' Safety Applications
Bibliographic record
Abstract
Vehicular Ad hoc Networks (VANETs) differ from the predominant models of Mobile Ad hoc Networks (MANET) due to their high speed, mobility constraints and drivers' behaviour. Most researches on analyzing the performance of VANETs' applications done without taking into account the vehicles' high dynamics assuming stationary distribution of vehicles on the road. They assume that all vehicles within the range of the transmitter will receive the transmitted packets successfully. While vehicles near the outer edge of the transmitter's range are more vulnerable to cross the boundary before they receive the packet completely. They also used Most Forward Within Range (MFR) to forward the message from one hop to the next. In this paper, we develop a new mobility model to derive the number of vehicles on the road and the probability of receiving the broadcasted packets successfully from all vehicles within the range of the transmitter. We also derive the probability of multi hop connectivity taking into account the location of relay vehicles and prove that MFR is not a valid scheme in VANETs.
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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) | 0.001 | 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 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".