Energy management control for supplying partner selection protocol in mobile peer‐to‐peer three‐dimensional streaming
Bibliographic record
Abstract
SUMMARY In recent years, three‐dimensional (3D) streaming over thin mobile devices has received very little attention from the research community. The 3D streaming‐based class of applications uses either a centralized or a distributed approach. whereas the former presents several drawbacks such as latency, server's bottleneck, and networks congestion; the latter, that is, peer‐to‐peer, has to deal with the supplying partner issue that consists in selecting the source that will be responsible for streaming the required 3D data. Given that frequent 3D streaming overuses the mobile device's resources and dissipates its energy, the energy factor should be taken into consideration during the selection of the supplier. In this paper, we propose two energy management control protocols for supplying partner selection in peer‐to‐peer 3D streaming that we refer to as one‐level energy‐based and two‐level energy‐based. We also propose a new source load estimator that takes into account two factors namely the source's residual energy and its number of served requests. In one‐level energy‐based, the requester uses the source's load information to prioritize the sources in an ascending order and distribute the requests starting with the least loaded sources. In two‐level energy‐based, both the requester and the supplier have important and complementary roles and participate in the energy management control. We then report on the performance evaluation of our energy management control protocols using an extensive set of simulation experiments with the NS2 tool. Copyright © 2011 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".