Bibliographic record
Abstract
The research efforts in the area of ad-hoc networks have evolved rapidly since the establishment of the IETF MANET workgroup. The major focal points have been the routing protocols, and the ongoing enhancements to the hardware of personal devices. The QoS research in ad-hoc networks has been getting less attention, compared to routing for instance, and therefore, evolved slower. Almost all of the QoS research efforts in ad-hoc networks have been focused so far on solutions within the ad-hoc network. We are interested in raising awareness to the end-to-end QoS problems when one of the communication endpoints is inside the ad-hoc network, and the other endpoint is outside. We present a unique view of the QoS in ad-hoc networks and we propose a QoS solution that operates on the gateway to the access network. The proposed gateway has a flexible design that links QoS models running on an ad-hoc network with QoS models running on the fixed structure access network. The proposal follows classical gateway design approaches to facilitate a lightweight implementation and to provide an integrated end-to-end QoS solution. Our results show that the use of our solution leads to substantial increase in effective bandwidth associated with relative decrease in bandwidth variations and end-to-end delays. In this paper, we show these enhancements, analyze the results and comment on the behavior of the proposed gateway in various operational scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".