Adaptive User-Centric Traffic Steering Decision-Making in Multi-RAT IoV Network
Bibliographic record
Abstract
Intelligent Transportation Systems (ITS) are advancing alongside the Internet of Vehicles (IoV), enabling realtime data communication to improve traffic management, safety, autonomous driving, and comfort services. Effective steering of this data to the appropriate radio access technology (RAT) is essential for enhancing service delivery and user experience. In high-mobile and ultra-dense multi-RAT IoV environments, selecting the optimal RAT for traffic steering (TS) is critical based on real-time dynamic network conditions. A usercentric approach, driven by adaptive learning, can optimize TS decisions to meet vehicle-specific needs. This paper presents a high-mobility, ultra-density-aware adaptive learning-oriented approach for TS decision-making in multi-RAT IoV networks, named TSMD-AS. TSMD-AS utilizes the adaptive Actor-Critic SARSA Reinforcement Learning (ACS-RL) algorithm to dynamically determine the optimal RAT (5 G or 4 G) to steer traffic for each vehicle. Simulation results demonstrate that TSMD-AS significantly enhances TS efficiency, leading to higher throughput, reduced packet loss, and lower latency, thereby improving service delivery and overall user experience.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".