Safety context-aware congestion control for vehicular broadcast networks
Bibliographic record
Abstract
In order for the large-scale realization of vehicular networks to be feasible, the problem of congestion control must be addressed to ensure the reliability of safety applications. The latter rely on single-hop broadcasts of safety packets in the control channel to acquire up-to-date knowledge of the local neighbourhood. However, high transmission ranges of onboard radios and the highly dynamic mobility of vehicles may result in fast-forming pockets of high node density in the network. Subsequently, the excessive load caused by safety packets broadcasts may degrade the network performance and subsequently reduce the level safety provided by applications. Existing congestion control schemes in the literature aim to reach a fair rationing of available channel resources throughout the network. However, a particular vehicle, depending on its distance and relative velocity with respect to its neighbours may require less or more network resources than another vehicle to achieve the same level of safety benefit. We examine the problem of adapting the probability of transmission of each node under a slotted p-persistent vehicular broadcast medium access control (MAC) scheme. A network utility maximization (NUM) problem is formulated, in which utility incorporates both the expected delay and a notion of safety benefit. A distributed algorithm is proposed to solve this problem in a decentralized manner and its performance is studied through simulations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".