Intelligent Optimization Algorithm for Green IoV Networks Based on SSA
Bibliographic record
Abstract
The rapid development of the Internet of Vehicles (IoV) has been enabled by sixth-generation (6G) mobile communication and artificial intelligence technologies. However, with the explosive growth of vehicle big data. However, big data transmission consumes a significant amount of energy. Green IoV networks have received increasing attention. Power consumption optimization plays an important role in green IoV networks. Using decode-and-forward (DF) relaying, we investigate the intelligent power allocation optimization of green cooperative IoV networks. Based on the characteristics of mobile channels, we employ outage probability (OP) criterion to analyze communication performance of green cooperative connected IoV networks from a mathematical perspective. Novel OP mathematical expressions are obtained. Using the obtained OP results, we investigate the problem of OP performance minimization via power allocation optimization. The complex non-convex problem of power allocation is formulated, as well as the optimization function. Based on the sparrow search algorithm (SSA), an intelligent optimization algorithm is proposed to solve the above complex problem. The proposed SSA approach is compared to the state-of-the-art intelligence algorithms, and our proposed algorithm can achieve a shorter running time and a good OP performance, thereby enhancing the system's efficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".