Communication-efficient decentralized hypothesis testing for sensor networks with minimum fusion center feedback
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
A new decentralized hypothesis detection framework is developed, where local sensors are memoryless, receive independent observations, and minimum feedback from the fusion center. In addition to the standard criterion of minimizing detection delay under error probability constraints, an additional constraint on the number of communications between local sensors and the fusion center is introduced here. This communication metric is able to reflect both the cost of establishing communication links as well as overall energy consumption over time. The proposed detection scheme minimizes detection delay with constraints on both error probabilities and on the number of communications. Unfortunately, optimality requires derivation of time-delay distributions of reports to the fusion center and solution of nonlinear equations. Under certain approximations, a Poisson arrival model is shown to yield a tractable solution. The efficiency of the proposed algorithm is quantified analytically in comparison to centralized detection and is shown to be in agreement with simulation results.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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