Online Resource Allocation in Internet of Vehicles Using Topology Attribute-Aware Genetic Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Virtual resource allocation, widely referred to as Virtual Network Embedding (VNE), has received increasing attention from both industry and academia. In fact, VNE has ubiquitously become a technological leap in Internet of Vehicles (IoV) which is a fundamental framework for the anticipated success of future intelligent transportation. The general VNE problem has been shown to be NP-hard [1], [2] and finding an optimal VNE in a dynamic environment like IoV is even more challenging. In fact, research on VNE in dynamic environments, where connected moving vehicles act as substrate nodes to provision requested services, is still in its nascent stages. As a result, this paper proposes a Genetic Algorithm (GA) assisted by a novel fitness function considering critical network topological attributes and resource constraints for dealing with the online VNE problem considering vehicle mobility. Simulation results based on the Random Waypoint (RWP) mobility model indicate that the proposed algorithm achieves better performance compared to several existing VNE algorithms.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it