SARSA RL for Edge Connectivity Management in Vehicular Edge Networks
Bibliographic record
Abstract
Vehicular network connectivity within Intelligent Transport Systems (ITS) is essential for enabling seamless data and resource sharing, including transmitting critical safety messages, traffic management information, entertainment, and comfort services. This connectivity enhances the user experience by supporting complex interactions between vehicles and infrastructure in dynamic network environments. Edge connectivity management has recently gained attention for maintaining connection stability while managing complex models effectively. In this context, connectivity refers to vehicles' ability to maintain a stable and robust link with network resources and other vehicles for optimal data exchange. In this paper, we propose an edge connectivity management approach, the Edge Connectivity Estimation Model ECEM, aimed at ensuring connection stability and strength. We design and implement the SARSA Reinforcement Learning (RL) algorithm to assess and estimate the overall connection reliability, determining the optimal vehicular edge - a selective combination of vehicles within groups - to ensure superior connection strength for data and resource sharing, even in high-mobility scenarios. This estimation process helps identify the most suitable edge to meet the data-sharing requirements for each vehicle. Our approach considers multiple parameters, including mobility, application parameters, and network density. Extensive realistic simulations have demonstrated that our proposed approach outperforms existing methods by reducing packet loss and delay while increasing throughput.
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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 | 0.000 |
| 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".