Fuzzy-Based Dynamic Priority-Driven Allocation for Internet of Vehicles
Bibliographic record
Abstract
Vehicle Fog Computing (VFC) enables enhancing a vehicle's processing capabilities and providing support services and applications for intelligent transportation. VFC has become more critical and suited for delay-sensitive applications because of its low-latency feature. Vehicles must overcome significant difficulties to match the needed services and perform duties successfully. Several ways have been proposed to pool idle vehicle resources cooperatively, with just a handful focusing on priority mechanisms. We focus on allocating resources hierarchically depending on priority. Priority is assigned to resource requests depending on vehicle characteristics such as deadlines, distances, and mobility factors. For each vehicle characteristic, dynamic thresholds are calculated using fuzzy membership functions. A priority queue identifies and assigns managed resources under demands and availability. By decreasing the time a service request spends in the system queue and guaranteeing high throughput through appropriate resource allocation, our suggested solution enables meeting QoS criteria. Simulated tests demonstrated that response times and overall costs for servicing requests were reduced in simulated large-scale urban settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".