A probabilistic model for message propagation in two-dimensional vehicular ad-hoc networks
Bibliographic record
Abstract
Vehicular ad-hoc networks (VANET) promise to enhance the road safety and travel comfort significantly in both highway and city scenarios. Message propagation, either for emergency or pleasure purposes, constitutes a major category of VANET applications, and is particularly challenging in infrastructure-less vehicle-to-vehicle communication scenarios. In this paper, we study the connectivity property of message propagation in two-dimensional VANET. We first derive the exact expression for the average size of the connected components in the one-dimensional case, i.e., messages propagating along a main street, and give a close approximation to the size distribution. We further derive the connectivity of message propagation in the two-dimensional ladder case, i.e., covering the main and two side streets, and formulate the problem for the two-dimensional lattice case to cover all the blocks in a district. Extensive simulation has been conducted to verify the analytical model and provide further insights in message propagation with and without geographic constraints, respectively. The simulation results show the efficacy of the model and the tradeoff between these two message forwarding strategies, and provide guidelines for future network planning and protocol development.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".