Bibliographic record
Abstract
P2P applications are enormously popular on the Internet and their uses vary from file sharing to Voice-over-IP to gaming and more. Increasingly, users are moving toward wireless networked devices and wish to continue using P2P applications in these new environments. MANETs are expected to grow in use as wireless mesh and 4G networks increase in popularity. P2P and MANETs share some similarities, such as self-organization, dynamism, and resilience to failure, but it is necessary that P2P algorithms should take advantage of the realities of MANETs. In P2P networks, the overlay peers must form a topology of connections between themselves and this topology should reflect the underlying network in order to reduce delay and energy consumption. We study the results of a game-theoretic topology control algorithm which considers energy and distance between nodes in a P2P network running atop a MANET. We find that the minimum cost topologies are very highly connected and thus resilient, but in most cases the topologies do not stabilize even without peer mobility or churn.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".