A Novel Optimized Caching Technique for Mobile Gnutella Based Network to Support Large-Scale Collaborative Virtual Environment
Bibliographic record
Abstract
Collaborative virtual environments (CVEs) such as massive multi-user 3D games and military training environments can place strict requirements on network when participating users are sharing the 3D virtual environment through mobile devices in an ad-hoc network. This paper presents an optimization mechanism for Gnutella network to better meet the mobility and the network requirements of collaborative virtual environments (CVEs). With the rapidly increasing use of mobile personal devices, we are facing new challenges and opportunities for making efficient mobile collaborative virtual environments (MCVEs) applications based on Gnutella network. The proposed work comprises an enhancement of the Gnutella network through an efficient overlay formation protocol, and a novel caching optimization technique implemented in specific nodes selected through a Gnutella Ultrapeer system (GUS) mechanism. The resulting approach should overcome several problems including flooding and the limited capacity of mobile devices. In order to evaluate our designed protocols, we run the system over several ad-hoc routing protocols, simulation results shows that each ad-hoc routing protocol performs well only under specific simulation scenarios.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 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".