Enhancing the DSRC reliability to allow the coexistence of VANET's applications
Bibliographic record
Abstract
The Dedicated Short Range Communication (DSRC) technology has been adopted by the IEEE community to enable safety and non-safety application for Vehicular Ad hoc Networks (VANETs). To better serve these two classes of applications, the DSRC standard divides the bandwidth into seven channels. One channel, called control channel (CCH) to serve safety applications and the other six channels, called service channels (SCHs) to serve non-safety applications. The DSRC standard specifies a channel switching scheme to allow vehicles to alternate between these two classes of application. The standard also recommends that vehicles should visit the CCH every 100ms, called Synchronization Interval (SI), to send and receive their status messages. It is highly desirable that these status messages be delivered to the neighbouring vehicles reliably and within an acceptable delay bound. It is obvious that increasing the time share of the CCH from the SI will increase the reliability of safety applications. In this paper, we will optimize the control channel access such that safety applications have a high successful transmission rate within their share of the SI interval. Moreover, a new algorithm, called Optimal Channel Access (OCA), will be introduced to enhance the performance of the DSRC while keeping the CCH I as small as possible. Hence non-safety applications will have a fair share of the DSRC bandwidth.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".