Fog Computing Vehicular Network Resource Management Based on Chemical Reaction Optimization
Bibliographic record
Abstract
The Internet of vehicles (IoV) provides strong support for ensuring the diversification of urban transportation as the core of next generation intelligent transportation. However, with the rapid growth of vehicle numbers and mobile data, traditional IoV fails to meet the real-time and reliable communication requirements of modern intelligent transportation due to its singleness and low flexibility. In this paper, we study the resource management issues in the IoV, which aim to optimize the energy efficiency of the system. Meanwhile, a non-orthogonal multiple access (NOMA)-based fog computing vehicular (FCV) network architecture is proposed. By splitting the resource management problem into two subproblems of subchannel and power allocation, chemical reaction optimization (CRO) algorithm and real-coded chemical reaction optimization (RCCRO) algorithm are utilized to solve subchannel and power allocation problem, respectively. Also fog computing (FC) technology is introduced to improve local storage and computing capabilities in IoV. Simulation results prove the effectiveness of the proposed scheme.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".