Virtual Resource Composition for Allocation Management in VCC Networks
Bibliographic record
Abstract
Vehicular Ad-hoc Networks (VANETs) have contributed significantly towards improving road traffic management and safety. VANETs, integrated with Vehicular Clouds, enable underutilized vehicular resources for efficient resource management, fulfilling service requests. This work introduces the concept of clustering resources from nearby vehicles to form Combined Resource Units (CRUs). These units contribute to fulfilling user service requirements while avoiding the difficult task of matching user requests with the available resources of an individual vehicle. CRU composition is helpful, especially for the heterogeneity of vehicles. The vehicle resources are clustered into CRUs based on three different sized pools, making the service matching process more time-efficient. Previous works have adopted stochastic models for resource clustering configurations. However, this paper adopts distinct search algorithms for CRU composition, which are computationally less complex. Results showed that light-weight search algorithms, such as SSA, achieved close to 80% of resource availability without over-assembling CRUs in higher density scenarios.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".