Priority-Based Resource Allocation Model for Vehicular Fog Computing
Bibliographic record
Abstract
Vehicle Fog Computing (VFC) provides opportunities to enhance a vehicle’s processing capability as well as support services and applications for intelligent transportation. Due to its low-latency characteristic, VFC has become progressively valuable and appropriate for delay-sensitive applications. Vehicles must overcome substantial obstacles to match the required services and carry out jobs effectively. Several methods have attempted cooperatively pooling idle vehicle resources, and just a few have looked at priority techniques. We concentrate on hierarchically allocating resources based on priorities. Priority is given to resource requests based on some attributes of the vehicle, such as deadlines, distances, and mobility considerations. Dynamic thresholds are determined using the fuzzy membership functions for each vehicular attribute. Prioritization is arranged using a priority queue to identify and assign managed resources under requests and availability. Our proposed method enables fulfilling QoS standards by reducing the length of time a service request spends in the system queue and ensuring high throughput through effective resource allocation. Simulated evaluations revealed decreased response times and total costs for servicing requests in large-scale urban scenarios.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".