A Novel Cloudlet-Dwell-Time Estimation Method for Assisting Vehicular Edge Computing Applications
Bibliographic record
Abstract
Recently, to improve the efficiency and safety of the transportation system that is severely affected by the increasing traffic demand, the Internet-of-Vehicles (IoVs)/Vehicular Networks (VNets) have received more and more attention because it can effectively improve the ability of the participants in the transportation system to perceive the traffic environment around them through Vehicle-to-everything (V2X) technique. Moreover, V2X also makes it possible to share computing and storage power between vehicles, which further promotes the development of vehicular edge computing (VEC). However, due to the highly dynamic nature of the VNet's topology, based on the instant traffic flow condition, how to determine whether the vehicles on a given road can form a relatively stable cloudlet with certain computing or data storage capabilities to support certain VEC applications becomes a crucial task that needs to be solved. Therefore, in this paper, we proposed a Cloudlet-Dwell-time (CDT) estimation method to theoretically derive some essential parameters for implementing VEC applications, i.e., the vehicular cloudlet existence probability and its corresponding dwell-time. We further demonstrate the results of the proposed work based on traffic flow data chosen from the England Highways data set.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".