Determining Fuzzy Link Quality Membership Functions in Wireless Sensor Networks
Bibliographic record
Abstract
Wireless Sensor Network routing protocols rely on the estimation of the quality of the links between nodes to determine a suitable path from the data source nodes to a data-collecting node. Several link estimators have been proposed, but most of these use only one link property. Fuzzy logic based link quality estimators have been recently proposed which consider a number of link quality metrics. In this work, we implemented the Fuzzy logic based link estimator in the WiseRoute routing protocol, which is a collection style protocol in MiXiM. We also present an experimental approach to determine a suitable fuzzy membership function based on varying the shape of the fuzzy set for a multipath wireless sensor network scenario and choosing an optimum shape that maximizes the Packet Delivery Ratio of the network. The computed fuzzy set membership functions were evaluated against an existing fuzzy link quality estimator under typical scenarios and it is shown the performance of the experimental optimal membership function was better in terms of packet reception ratio and end-to-end delay.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".