Extending <scp>P</scp>2<scp>PMesh</scp>: topology‐aware schemes for efficient peer‐to‐peer data sharing in wireless mesh networks
Bibliographic record
Abstract
ABSTRACT Wireless mesh networks (WMNs) have emerged as a promising technology that provides low‐cost broadband access to the Internet for fixed and mobile wireless end users. An orthogonal evolution in computer networking has been the rise of peer‐to‐peer (P2P) applications such as P2P data sharing. It is of interest to enable effective P2P data sharing in this type of networks. Conventional P2P data sharing systems are not cognizant of the underlying network topology and therefore suffer from inefficiency. We argue for dual‐layer mesh network architecture with support from wireless mesh routers for P2P applications. The main contribution of this paper is P2PMesh: a topology‐aware system that provides combined architecture and efficient schemes for enabling efficient P2P data sharing in WMNs. The P2PMesh architecture utilizes three schemes: (i) an efficient content lookup that mitigates traffic load imbalance at mesh routers; (ii) an efficient establishment of download paths; and (iii) a data transfer protocol for multi‐hop wireless networks with limited capacity. We note here that the path establishment and data transfer schemes are specific to P2P traffic and that other traffic would use routes determined by the default routing protocol in the WMN. Simulation results suggest that P2PMesh has the potential to improve the performance of P2P applications in a wireless multi‐hop setting; specifically, we focused on data sharing, but other P2P applications can also be supported by this approach. 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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".