A Novel Online Machine Learning Based RSU Prediction Scheme for Intelligent Vehicular Networks
Bibliographic record
Abstract
Wireless networks development to support the highly dynamic vehicular environment pose several significant challenges for vehicular network services and applications, in efforts to guarantee seamless communication. Intelligent Vehicular Networks goal is to provide high-quality services that can learn and forecast clients' needs and intentions. Machine Learning (ML) is one type of artificial intelligence that can be effective in utilizing the vehicular network's data to predict users movements and allocate resources ahead of time. In this paper, we propose a novel online ML-based Roadside Unit (RSU) prediction scheme for mobility management in Vehicular Networks, to provide seamless mobile connectivity to vehicles and enhance the performance of the prediction model. An Online Probabilistic Neural Network (O-PNN) prediction model is designed and adjusted for VANETs mobile IP protocol. Extensive simulation experiments were performed on the Network Simulator NS-2, and the performance of the prediction model is studied with different traffic and mobility scenarios. Our results showed a high accuracy rate in comparison to several other machine learning models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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".