Congestion Control for Vehicular Networks With Safety-Awareness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it