Co-existence of Evolutionary Mixed-Bias Scheduling with Quiescence and IEEE 802.11 DCF for Wireless Mesh Networks
Bibliographic record
Abstract
The evolutionary programming mixed bias scheduling has been shown to provide significantly reduced end-to-end delay while maintaining comparable packet delivery ratio when evaluated using simulation experiments compared to the current IEEE 802.11 DCF standard. In this paper, proposed is an enhanced MB-EP algorithm which includes a new quiescent state called MB-EP-Q. This new approach is evaluated using network simulation with respect to throughput and delay. The new approach was found to have similar performance to the existing MB-EP approach. Furthermore to demonstrate that the MB-EP-Q approach can co-exist with existing IEEE 802.11 DCF mechanisms another experiment is performed where a portion of the routers are running the MB-EP-Q algorithm while the rest run IEEE 802.11 DCF. The results show an improvement in performance even when a small proportion of the nodes are running the MB-EP-Q algorithm. This result is important since it shows that the MB-EP approaches can be applied to existing deployments gradually and with lower cost than other competing approaches which may require the entire network infrastructure to be modified.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".