A Novel Gnutella Application Layer Multicast Protocol for Collaborative Virtual Environments over Mobile Ad-Hoc Networks
Bibliographic record
Abstract
Collaborative virtual environments (CVEs) such as massive multiuser 3D games and military training environments can place strict requirements on networks when participating users share the 3D virtual environment through mobile devices in an ad-hoc network. In this paper, the authors show how a CVE application can benefit from the application layer multicast when deployed on the Gnutella peer-to-peer network over an ad-hoc network. The authors propose a protocol called GALM (Gnutella application layer multicast). GALM requires no infrastructure support such as a multicast router to maintain the group state. It has the following characteristics: a) it is adaptable to mobility and network group size by managing the mobile device resources by using gateway node, b) it is reliable; the CVE application can choose at a running time the adequate transport protocol for each data type - for example, using TCP for scene and object data and RTP to send video and audio data, c) it is independent from the lower layer; any link failure or mobility in the physical layer will not affect the application layer, eliminating the need to perform a multicast tree reconfiguration, d) it has link quality; therefore, a cross layer can be used between the network and the application layer in order to provide optimal paths in the multicast tree configuration process. In addition, the protocol handles tolerance to mobility and multicast tree recovery using a smart logical Gnutella network that is based on a novel discovery technique in which mobile nodes are found by means of both their state and position in the CVE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".