MOSAIC - A Mobile Peer-to-Peer Networks-Based 3D Streaming Supplying Partner Protocol
Bibliographic record
Abstract
The rapid spread of wireless mobile devices and the advances of wireless communication have fueled the interest about streaming 3D graphics on mobile devices to be used in augmented reality based classes of applications. In these types of applications, a real world is mapped into the virtual world and thin mobile devices are employed to navigate in the virtual simulated environment (VE). In evidence, one of the prime difficulties in 3D streaming over thin mobile devices consists of the limited mobile resources and capabilities, i.e., low processing power, limited storage capacity, limited graphics' hardware and graphics' accelerator making it very difficult for mobile devices to render and process large and complex 3D scenes. So far, a significant body of work has been dedicated to the challenges of mobile networks-based 3D streaming such as streaming performance, and bandwidth limitation. On the downside, very few studies have been committed to the mobile supplying partner strategies aiming at determining the peer that owns the correct information and that possesses enough bandwidth to send the required data quickly and efficiently to other peers in need. In this paper, we propose MOSAIC, our supplying partner strategy protocol for mobile networks-based 3D streaming. MOSAIC is based on the quick discovery of multiple supplying partners, by optimizing the time required by peers to acquire data, avoiding unnecessary messages propagation and network congestion, and decreasing the network bandwidth over utilization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".