An Architecture for Hierarchical Software-Defined Vehicular Networks
Bibliographic record
Abstract
With the recent advances in the telecommunications and auto industries, we have witnessed growing interest in ITS, of which VANETs are an essential component. SDN can bring advantages to ITS through its ability to provide flexibility and programmability to networks through a logically centralized controller entity that has a comprehensive view of the network. However, as the SDN paradigm initially had fixed networks in mind, adapting it to work on VANETs requires some changes to address particular characteristics of this kind of scenario, such as the high mobility of its nodes. There has been initial work on bringing SDN concepts to vehicular networks to expand its abilities to provide applications and services through the increased flexibility, but most of these studies do not directly tackle the issue of loss of connectivity with said controller entity. In this article, we propose a hierarchical SDN-based vehicular architecture that aims to have improved performance in the situation of loss of connection with the central SDN controller. Simulation results show that our proposal outperforms traditional routing protocols in the scenario where there is no coordination from the central SDN controller.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".