A Novel Centralized Cloud-Based Mobile Data Rollover Management
Bibliographic record
Abstract
Mobile service providers (SPs) offer various data plans to satisfy the needs of their customers and to maximize their own profits. One of these plans is unlimited Internet access. However, in practice there is a concern for customers who do not want to pay premium prices for unlimited plans, as exceeding the maximum data allowance in a limited plan may result in a high penalty. But when users travel abroad, roaming services based on current technology have limited coverage and high prices, which do not meet customer expectations. A new paradigm to overcome these problems is a virtual SIM card (V-SIM), which can be established based on 5G network architecture. This new approach uses cloud technology to enable customers to have the data they require wherever they want. In this work, we propose a cloud-based service for users to provide data all over the world by designing the novel architecture of V-SIM technology based on 5G communication systems. More precisely, a new scheme, called a centralized cloud data center, or `data-pool' for short, is designed to present data rent and release facilities for mobile users any time and everywhere. The benefits of the proposed scheme for both SPs and their customers are modeled and analyzed based on real-world experiments. Finally, by considering the user's behavior, the performance of the proposed approach is compared to the conventional method in terms of the monthly data usage, monthly data rent/offload status, and the payment with and without considering the proposed method.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".