Adapting to the driving context in congestion control for vehicular networks
Bibliographic record
Abstract
Vehicular safety applications require reliable reception of basic safety messages (BSMs), which are broadcast from their neighbours. These messages contain position, mobility, and other information about the vehicle state critical for the operation of envisioned safety-enhancing applications. The scalability of future vehicular networks depend on a suitable congestion control method to handle instances where high node density results in excessive load on the shared control channel, degrading network performance and reducing the safety level of vehicles. When network congestion is detected, these algorithms reduce the offered load by adapting transmission parameters such as the rate and the power. Early works aimed for a fair distribution of network resources in their adaptation of transmission parameters. More recent works recognized that different driving contexts may warrant preferential allocation of resources to nodes in more potential danger. However, existing methods allow users to relate the driving context to a corresponding share of the network resource, rather than a certain quality of service (QoS) level. In this work, we propose a method of representing the driving context as a maximum delay profile. A probabilistic bound on the violation of this delay profile form a QoS requirement between neighbouring nodes. We propose a simple way to incorporate these constraints into an additive-increase multiplicative-decrease congestion control algorithm, which requires minimal amount of packet header overhead. We study its performance by comparing it with a recent AIMD algorithm through simulations and demonstrate its improved fairness in terms of constraint satisfaction probability.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 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".