The Full ESWAN Destination-Based Approach: Operations And Evaluation
Bibliographic record
Abstract
In response to the growing need to support better than best-effort (BE) quality of service (QoS) in mobile ad-hoc and sensor networks, many QoS models have been proposed. SWAN independent QoS model is introduced to operate on wireless ad-hoc networks. As a cross layer QoS model, SWAN is flexible and may run over any routing protocol or Media Access Control (MAC) layers. SWAN provides some advantages over competitive models However, SWAN is vulnerable to problems related to mobility and false admission. The original SWAN model discusses the two problems as part of a dynamic regulation of real-time flows, and introduced two solutions, namely source and network-based regulation algorithms. This paper criticizes both regulation algorithms and show why destination-based algorithm selects real-time victim flows in a better way. Then we provide test results to analyze and evaluate the destination-based approach.
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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 | 0.015 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.017 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".