Comparative Evaluation Approach for Spectrum Sensing in Cognitive Wireless Sensor Networks (C-WSNs)
Bibliographic record
Abstract
In spite of conventional wireless sensor network (WSN) nodes, which only sense environment, new developments of WSN, e.g., cognitive-WSN (C-WSN), wireless multimedia sensor network, wireless actor network, and cryptography in WSN, need to run algorithms on their nodes. But, there is no method or appropriate criteria to compare proposed algorithms or investigate whether or not an algorithm is suitable for these next-generation WSNs. Indeed, due to resource constraints in WSN nodes and lack of an evaluation method, most high-performance algorithms are renounced for the next-generation WSNs without any investigation. For example, many references have proposed low-performance, low-complexity energy detection (ED) method for C-WSNs without any analysis or evaluation, only because ED has the least complexity among all spectrum sensing methods. In this paper, we propose an appropriate set of criteria and a comparative method for evaluating algorithms in next-generation WSNs. Then, we develop the method for evaluation of spectrum sensing algorithms in C-WSNs. Finally, we investigate ED, the multitaper method (MTM), and united MTM (UMTM) spectrum sensing algorithms based on our comparative approach.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".