Optimality and stability of event triggered consensus state estimation for wireless sensor networks
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
This paper presents distributed state estimation methods through wireless sensor networks with event triggered communication protocols among the sensors. Optimal consensus filters are derived which apply to generic non-uniform and asynchronous information exchange scenarios among neighboring sensors. To obtain a scalable covariance propagation algorithm, the optimal filter is approximated by a suboptimal filter. Homogeneous detection criteria are designed on each sensor node to determine the broadcasting instants. Thus, a consensus on state estimates is reached with all estimator sensors for the suboptimal consensus filter. The purpose of event detection is to achieve energy efficient operation by reducing unnecessary interactions among the neighboring sensors. In addition, the performance of the proposed state estimation algorithm is validated using a simulation example.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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