Decentralized Adaptive Filtering Algorithms for Sensor Activation in an Unattended Ground Sensor Network
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Bibliographic record
Abstract
We present decentralized adaptive filtering algorithms for sensor activation control in an unattended ground sensor network (UGSN) comprised of ZigBee-enabled nodes. Nodes monitor their environment in a low-power ldquosleeprdquo mode, until an intruder is detected, then must decide whether to enter a full-power monitoring and transmission mode if their estimated average performance for activation outweighs their energy cost. The tradeoff is formulated in terms of the energy required to transmit data using the ZigBee protocol, probability of successful transmission, and the expected marginal increase in global utility resulting from a report, all of which depend on the activity of nearby sensor nodes. Since activation control is decentralized, and utilities are codependent, the adaptive filtering/stochastic approximation algorithms that we propose for sensor activation are based on game theoretic principles. We show that if each sensor operates according to this algorithm, the entire network is capable of actively tracking the correlated equilibrium set of the underlying game, which varies with target motion, node failures, or intentional parameter adjustments. We analyze the convergence and tracking properties of the adaptive filtering algorithms using differential inclusions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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