An efficient method for mobile big data transfer over HetNet in emerging 5G systems
Bibliographic record
Abstract
Recently, mobile cloud computing (MCC) has arisen as a promising technology to augment the user equipment (UE)' capabilities in emerging 5G systems by wirelessly transferring the computation-burden from it to the resource-rich cloud computing centers. In this setting, the massive data transfer over a standalone wireless network becomes a challenging task due to the expected increased latency over the wireless channels associated with the remote cloud data centers. This paper proposes an efficient data transfer method for mobile big data along with a data correction technique to cope with the problem of failure to retrieve the data chunks in the cloud. The proposed algorithm exploits the overlapping feature of heterogeneous wireless networks (HetNets) to expedite the mobile big data transfer between the UE and the cloud by splitting the data into a number of smaller chunks which are transmitted simultaneously over the wireless links. Simulation results are provided, showing that our proposed method outperforms the baseline data storage method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".