Throughput Analysis of Vehicular Internet Access via Roadside WiFi Hotspot
Bibliographic record
Abstract
Roadside WiFi network has been widely considered for the drive-thru internet access. However, the data throughput is significantly affected by the access procedure that includes the steps of association, user authentication and assignment of network parameters such as internet protocol address. In this paper, we investigate the throughput performance of the drive-thru internet considering the impact of the access procedure. Particularly, a three-dimensional Markov model is proposed to analyze the relationship between the vehicle's location and the accomplishment of the access procedure that involves the exchange of the management frames under different conditions, such as number of contending WiFi clients, number of management frames, their drop rate due to channel error, etc. We also study two access schemes, namely Hotspot 2.0 and WPA2-PSK, to show how different access protocols can affect the throughput performance. We conduct extensive simulations to validate our analysis, which could provide provident insight for future development of vehicular networks.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".