Connectivity-based Fog Structure Management for Software-defined Vehicular Networks
Bibliographic record
Abstract
Vehicle Fog computing combines intelligent and connected vehicles to form a mobile cloud. Several works have modelled link stability for data delivery in light of solving the issues originating from unstable vehicle connectivity. However, results have shown that some mobility patterns potentially misguide the uncertainty-based estimation process. We thus propose a region-based connectivity ranking strategy. A fog management approach dynamically defines and supervises regions delimited by vehicles; such regions are mapped over an urban centre. In addition, the model develops a software-defined vehicular network (SDVN) controller to select data from the vehicular heterogeneous network environment through V2X and C-V2X. Our model admits four parameters to describe vehicular connectivity, which evaluates vehicles’ potential for communication and performs dynamic vehicular clustering. The 5G and DSRC heterogeneous networks support a more precise connectivity model for vehicular classification. Simulated analyses allow observing vehicular mobility and connectivity data in real-time scenarios where the management efficiency of vehicular fog regions is assessed in SDVN context.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".