ODCRep: Origin–Destination-Based Content Replication for Vehicular Networks
Bibliographic record
Abstract
The evolution of vehicular network applications, from simple alert message exchange to more elaborate and sophisticated systems, boosts the need for content delivery solutions. A useful technique, in this case, is content replication, in which strategically selected vehicles replicate content and help in the delivery process. However, content replication is particularly challenging in vehicular networks, due to their special characteristics, such as highly dynamic topology, diverse density, and large-scale scenarios. Although there has been progress in routing and dissemination solutions for vehicular networks, few studies have concentrated on the content replication problem. To address this issue, we propose an origin-destination-based content replication (ODCRep) solution that focuses on balancing the number of replicas across the application area. Differently from existing solutions, ODCRep relies only on the origin-destination information and uses computationally efficient algorithms. Results from exhaustive simulations show that ODCRep can achieve high coverage, yet can also consume fewer resources than existing solutions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".