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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".