Energy conservation in clustered wireless sensor networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, results from Wald's equation and stochastic geometry are applied to the analysis of the energy expended in a homogeneous clustered Wireless Sensor Network (WSN). We determine the optimum number of clusterheads for minimising the energy expended by a single-hop clustered WSN in transmitting data to a sink using nonlinear and linear aggregation models, and include error control. Our model makes it possible to determine the optimum number of clusters given the node electronic energy expended, the type of aggregation employed, the propagation loss and the network geometry. The effect of these parameters on the optimum number of clusterheads is analysed. The analytical model is verified with simulations. We observe that, in some networks, clustering is not beneficial for minimising network energy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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