Joint Deterministic and Probabilistic Edge Caching Minimized Service Time for Cooperative Video Transmission in VANETs
Bibliographic record
Abstract
Video streaming in vehicular ad-hoc networks (VANETs) faces significant challenges due to the dynamic nature of vehicles, frequent disconnections, and huge demands for high data rate communications. These challenges make it longer for vehicle users (VUs) to complete their sessions in video applications and services (VASs). In this paper, we fully utilize the benefits of both deterministic and probabilistic edge caching (DPC) techniques for cooperative transmission to minimize the service time in VASs. To do so, a DPC optimization problem is formulated and solved for the optimal results of 1) caching placement in roadside units (RUs) and 2) caching probability in VUs under the constraint on caching storage resource. Genetic algorithms are modified to deal with the complexity of two types of optimization variables, i.e., integer variable for deterministic caching and real variable for probabilistic caching, and thus ensuring high stability and accuracy. Simulation results demonstrate that the DPC method outperforms the other conventional schemes in terms of service time while efficiently utilizing the storage of RUs and VUs. Important findings are also analyzed and discussed to provide more useful insights into the design of edge caching techniques for VASs in VANETs.
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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 | 0.000 | 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".