Content caching and replication schemes for peer-to-peer file sharing in wireless mesh networks
Bibliographic record
Abstract
Wireless Mesh Networks (WMNs) have emerged as an important technology in building next generation fixed wireless broadband networks that provide low cost Internet access for fixed and mobile users. An orthogonal evolution in computer networking has been the rise of Peer-to-Peer (P2P) applications such as P2P file sharing. It is of interest to enable effective P2P file sharing in this type of networks. Our main contribution in this paper is innovative schemes for content caching and replication at mesh routers that enhance the performance of P2P file sharing in WMNs. We first motivate our proposed schemes by showing the impact of caching P2P content at mesh routers on the performance of P2P file sharing in WMNs. We then describe the design and operation of our content caching and replication schemes. Finally, we compare the performance of our proposed schemes against other existing schemes using simulations. We focus on P2P file sharing but other applications can also be supported by the proposed schemes.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 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".