Energy efficient spectrum aware clustering for cognitive sensor networks: CogLEACH-C
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
Combining Cognitive radio technology with wireless sensor networks has been introduced in the literature as a solution to the spectrum deficiency problem. Many clustering algorithms have been proposed for wireless sensor networks. However, most of them are not suitable for cognitive sensor networks as they operate on a fixed channel settings. In this work, we propose a low energy spectrum aware clustering algorithm, CogLEACH-C, based on CogLEACH algorithm in order to improve the performance in terms of system lifetime. CogLEACH-C uses not only the number of channels sensed idle by the node but also the node energy level in determining the probability for each node to be a cluster head. Hence, allows the base station to select K cluster heads that are in an optimal position for the nodes in the network.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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