MBP: Routing Metric Based on Probabilities for multi-radio multi-channel wireless mesh networks
Bibliographic record
Abstract
This paper addresses the problem of optimal route selection in wireless mesh networks (WMNs) where intra-flow and inter-flow interferences have a negative impact on the network performance. Several routing metrics have been proposed in the literature to find paths that minimize interferences and thus maximizes throughput. However, most of these metrics consider either inter-flow interferences or intra-flow interferences and a few consider both types of interferences. In this paper, we propose an efficient new routing metric, called MBP (Metric Based on Probabilities), that aims to choose routes with high throughput, low intra-flow and inter-flow interferences between a source and a destination. MBP is based on the Minimum Loss (ML) metric and a well known physical interference model. Simulation results show that MBP outperforms some existing metrics, namely hop count, WCETT and iAWARE, in terms of throughput, delay and packet loss.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".