Bibliographic record
Abstract
This paper explores the use of fuzzy logic for threshold buffers management in wireless ad hoc networks. This exploration is useful first, because of the dynamic nature of buffer occupancy and congestion at a node; second, because of the uncertainty of information in wireless ad hoc networks due to network mobility. The notion of threshold is practical for discarding data packets and adapting the traffic service depending on the occupancy of buffers. The threshold function has a significant influence on the performance of networks in terms of both packets average delay and throughput. We propose a fuzzy logic approach for threshold selection named (FuzzyCCG) in order to enhance the control of congestion. FuzzyCCG was studied under different mobility, channel, and traffic conditions. The results of simulations confirm that the proposed model can achieve low and stable end-to-end delay under different network scalability and mobility conditions. FuzzyCCG promises to be an efficient tool for reducing the delay of multimedia applications in wireless ad hoc networks.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".