Optimal data fusion of collaborative spectrum sensing under attack in cognitive radio networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cognitive radio networks allow opportunistic spectrum access and can significantly improve spectral efficiency. To achieve higher sensing accuracy, cognitive radio systems often require cooperation among secondary users. One of the most important aspects in collaborative spectrum sensing is the data fusion algorithm which combines the sensing results from secondary users to produce the final channel status hypothesis. However, plenty of factors may affect the performance of certain data fusion rule, for example, the individual sensing node's sensing accuracy, the number of involved nodes, and the like. If Spectrum Sensing Data Falsification (SSDF) attack exists, it will become more challenging to make proper data fusion. In this article, we first introduce framework, and then evaluate the data fusion rules in different scenarios through simulation examples. Finally, a Genetic Algorithm based optimal scheme is proposed to achieve better performance in all scenarios.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it