Congestion Control for Vehicular Networks With Safety-Awareness
Bibliographic record
Abstract
Vehicular safety applications require reliable and up-to-date knowledge of the local neighborhood. Under IEEE 802.11p, this is attained through single-hop broadcasts of safety beacons in the control channel. However, high transmission power and node mobility can cause regions of node density to form rapidly. In such situations, excessive load on the control channel must be avoided to prevent performance degradation for safety applications. Existing congestion control schemes aim to reach a fair distribution of available channel resources, but fail to account for the differing quality of service (QoS) requirements of vehicles in different driving contexts. This context depends on many factors, including the relative position and velocity of its neighbors. The problem of adapting each vehicle's transmission probability under a slotted p-persistent vehicular broadcast medium access control (MAC) protocol is formulated as a network utility maximization (NUM) problem which takes the driving context into account. A distributed algorithm is proposed to solve this problem in a decentralized manner, its convergence is analyzed, and its performance is evaluated 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".