Favorable Peer Supported Throughput Optimization in Wireless Mesh Network
Bibliographic record
Abstract
Peer selection strategy is one of the major challenges towards efficient peer-to-peer (P2P) system in 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. Here, we propose a novel optimization method to calculate the upper bound of the aggregate throughput for P2P over WMNs utilizing the underlying physical network information and optimal peer selection strategy. 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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".