Zone-based Popularity-Oriented Multi-Layer Content Caching in Vehicular Fogs
Bibliographic record
Abstract
Caching in vehicular fog infrastructure has been explored to deal with the problems related to vehicular networks, but it still needs efficient data management strategies to deal with high mobility and dynamic network topology. A multilayer caching approach enables faster data access and processing through a hierarchical structure that handles requests at an appropriate fog level, reducing latency and improving overall system efficiency. However, storing random content following the mobility of vehicles can result in cache pollution when overloaded with information that is unlikely to be used again, lowering cache hits and decreasing its effectiveness. Therefore, we propose a multi-layer distributive transient cache with adhering zone-based popularity-centric caching strategy to generate improved cache hits, reduce content request latency, and place cache content closer to the requester to provide better data availability. This fogoriented caching technique showed enhanced data management through comprehensive analyses of simulated realistic urban scenarios - an improvement of 2.2% of cache hits and a reduction of 1.7ms of fetching latency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".