Detecting the Defective Nodes in Wireless Sensor Networks Using the Nonlinear Consensus of Median
Why this work is in the frame
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Bibliographic record
Abstract
A local algorithm is proposed and analyzed to monitor the health of a wireless sensor network. Our previous algorithm, the average consensus-based algorithm, works based on the mean estimator. Therefore, it fails to detect the defective nodes in the presence of large outliers. However, the algorithm proposed in this paper works based on the median estimator, and is able to detect the defective nodes, even in the presence of a large number of outliers. To calculate the median in a distributed scheme, a nonlinear consensus algorithm is proposed. By applying the nonlinear consensus algorithm, all the nodes in the network compute the median iteratively, using only local communications. We show that the proposed algorithm is more robust than the previous algorithms in this area and outperforms all of them. The simulation results verify the effectiveness of the proposed algorithm in calculating the median in a distributed scheme and in detecting the defective 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 | 0.001 | 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 | 0.000 |
| 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