Explicit rate based transmission control in vehicle to infrastructure communications
Bibliographic record
Abstract
Vehicle to infrastructure communications can provide mobile users with a series of Internet services, such as video streaming, digital map downloading, database access, online gaming, and even safety services like accident alarm, traffic condition broadcast, etc., through fixed roadside units. However, the dynamics of communication environment and frequent changing topology critically challenge the design of an efficient transport layer protocol, which makes it difficult to guarantee diverse quality of service (QoS) requirements for various applications. In this paper, we present a novel transport scheme in infrastructure based vehicular networks, and aim to resolve some challenging issues such as source transfer rate adjustment, congestion avoidance, and fairness in end to end data communications. By precisely detecting packet losses and identifying various causes of these losses (for example, link disconnection, channel error, packet collision, buffer overflow), the proposed scheme adopts different reacting mechanisms to each of the losses. Moreover, it timely monitors the buffer size of the bottleneck RSU, and dynamically makes transfer rate feedbacks to the source nodes to avoids buffer overflow or vacancy. Finally, simulation results show that the scheme not only successfully reduces packet losses due to buffer overflow and link disconnection but also improves the utilization efficiency of channel resource.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".