Performance evaluation of mixed-bias scheduling schemes for wireless mesh networks
Bibliographic record
Abstract
Typically, peripheral nodes in a multi-hop wireless network experience poor performance from starvation, congestion, queue build-up and contention along the path towards internet gateways. We propose three adaptive methods for scheduling based on mixed-bias scheduling which aim to prioritise mesh routers near the gateways to ensure they can handle their own traffic and peripheral traffic. We also give an overview of the mixed-bias approach for scheduling. We then evaluate the performance of each technique in comparison with each other and the IEEE 802.11 distributed coordination function. Each solution is evaluated based on average packet delivery ratio and average end-to-end delay. Two experiments were performed to examine the performance. First, we studied the effect of varying the inter-arrival rate of the packets. Second we examined the effect of changing the number of sources. In all experiments, the proposed approaches perform at least as well or better than IEEE 802.11 DCF.
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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 | 0.002 | 0.000 |
| 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 | 0.001 |
| 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".