Penalty Function Method for Peer Selection over Wireless Mesh Network
Bibliographic record
Abstract
Appropriate peer selection from the discovered file holders plays a vital role for peer-to-peer (P2P) file sharing over wireless mesh networks (WMNs). When peers choose their own utility-maximizing strategies for coalition and peer formation, the solution is always sub-optimal. Peer formation, based on only application layer information, also results in inefficient use of network bandwidth. When multiple recipient-peers try to access the same file from same source-peer simultaneously, contention may occur on the shared wireless channel. On the discovery of multiple source-peers, corresponding recipient-peer may choose optimal source-peer in favor of increased network throughput. We formulate the joint peer selection and utility maximization problem as a mixed integer nonlinear programming (MINLP) framework. We also propose penalty-based heuristic genetic algorithm (GA) to solve the MINLP. The results show that our favorable-peer selection strategy results in higher aggregate throughput by selecting optimum source-peers with better load distribution and minimum interference.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".