A Link-Reliability-Based Approach to Providing QoS Support for VANETs
Bibliographic record
Abstract
Vehicular Ad Hoc Networks are a promising technology that provides several interesting new functionalities to drivers and passengers. These services are spread through a wide variety of applications, each with specific requirements in terms of latency, jitter and bandwidth, among other metrics. For this reason, it is crucial to provide mechanisms that can be used to offer different levels of communication quality in order to achieve reasonable performance in all services. In this paper, we demonstrate how link reliability may be used to support Quality of Service policies for any protocol based on unicast packets relay. This article is divided into two parts, the first of which describes how we estimate link reliability and evaluates how these estimations can be used to classify links into different groups with distinct quality. In the second part, this link reliability estimation model is used to group links into queues with different levels of expected transmission success ratios, which can be used to provide different quality levels depending on the requirements of each individual service. We show that the proposed mechanism adds little-to-none overhead to the overall network and provides an effective mean of supporting QoS in VANETs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".