How to Download More Data from Neighbors? A Metric for D2D Data Offloading Opportunity
Bibliographic record
Abstract
Mobile devices in close proximity can be connected in a device-to-device (D2D) manner to transfer digital objects (e.g., videos) to each other. By using D2D data offloading, mobile users can reduce the cost for data service from wireless cellular networks. However, due to users' mobility, the opportunity for a user to obtain his interested objects via D2D communication is transient. In this paper, we first propose an expected available duration (EAD) metric to evaluate the opportunity that an object can be downloaded by a user via D2D data offloading. The EAD metric takes into account the pairwise connectivity of users, social influence between users, diffusion of digital objects, and the time that users would like to wait for D2D data offloading. We then propose a distributed algorithm for a mobile device to determine the EAD of each object. Given a set of available objects in the neighborhood, a mobile device will first download the object that has the smallest EAD. We validate our model via trace-driven simulations. Results show that our proposed algorithm can effectively find the object that should be first downloaded. Comparing with existing schemes, our work can help users download more data via D2D data 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".