Mitigating Load Imbalance in Wireless Mesh Networks with Mixed Application Traffic Types
Bibliographic record
Abstract
Wireless Mesh Networks (WMNs) have been widely deployed as a new communication paradigm that can provide innovative services for a community (neighborhood, campus, etc.). WMNs are capable to provide both delay-sensitive services such as Voice over Internet Protocol (VoIP) and delay-insensitive services such as peer-to-peer file sharing. Network routing protocols in WMNs often employ the minimum-hops routing metric to provide Quality of Service (QoS) for the delay-sensitive traffic. However, this approach results in unbalanced traffic load at network mesh routers. In this paper, we propose the WMN-Balance: a content lookup algorithm for peer-to-peer file sharing over wireless mesh networks. The WMN-Balance enhances the content lookup routing on the overlay network, that involves mesh routers which support peer-to-peer file sharing, to mitigate the network unbalanced load.
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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 | 0.000 | 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 | 0.000 |
| 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".