Exploiting Hotspot-2.0 for Traffic Offloading in Mobile Networks
Bibliographic record
Abstract
WiFi networks can offload data traffic from congested cellular networks in a cost-effective way. However, it is difficult to perform WiFi offloading in mobile environments due to the complicated and time consuming access procedure of WiFi networks. In this article, we first investigate mobile traffic offloading by leveraging the HS-2.0 technique, which greatly simplifies the access procedure and provides a novel signaling diagram to enable automatic association and seamless roaming for mobile users. We then compare the legacy HS-1.0 with HS-2.0, and study the impacts of HS-2.0 on mobile traffic offloading by considering the pedestrian case and the drive-thru Internet case, respectively. We develop an HS-2.0 traffic offloading prototype and provide useful results through empirical measurements. Finally, we show the research issues for HS-2.0 mobile traffic offloading.
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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) | 0.000 | 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) | 0.002 | 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".