A Novel Statistical Model for Distributed Estimation in Wireless Sensor Networks
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Bibliographic record
Abstract
In this paper, we consider the problem of distributed parameter estimation in imperfect environments for wireless sensor networks (WSNs). By imperfect environments, we refer to distortions that can be caused by sensor noise, quantization noise and channel effect. A novel statistical model is proposed to quantify these errors in WSNs. The first and second order statistics are derived analytically. The estimator is then probability density function unaware. An analytical bound of the mean square error (MSE) performance at the fusion center is also derived. We further apply the proposed method to the power scheduling problem of WSNs. By formulating it as a convex optimization problem, an analytical solution is obtained. Simulation results show that the proposed approach outperforms the conventional distributed estimation methods. For the power scheduling application, the proposed method is shown to have an improved power saving compared to a classic method in the literature.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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