Analysis of Message Dissemination in Vehicular Networks
Bibliographic record
Abstract
Safety-critical applications form the main motivation for intelligent transportation systems (ITSs). Studying the major concerns in such applications, i.e., delay and reliability, through mathematical analysis is extremely beneficial because it enables us to design optimized schemes. Such analysis is, however, challenging due to the dynamics of such a network. In this paper, we present a mathematical model to study delay and reliability of emergency message dissemination in vehicular networks. First, a basic model is presented using simplified assumptions about the channel. Then, we enhance the model using Rician distribution as a realistic fading model for vehicular environment. We make some interesting observations from the presented model. First, the end-to-end reliability has a fairly fast transition over time, which we formally prove in this observation. The second observation from the analytical model confirms the fact that using the vehicle density on the road is a good metric for setting the right forwarding probability in vehicles. We exploit this conclusion and propose a completely distributed forwarding strategy. Simulation studies indicate that our model does capture the delay characteristics of vehicular networks. It also affirms the effectiveness of our warning dissemination scheme in terms of delay and single-hop reliability in comparison with other well-known routing methods. We believe that this is a promising step toward accurate characterization of communication delay and reliability in vehicular networks.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".