Decentralized Management of Sensors in a Multi-Attribute Environment Under Weak Network Congestion
Why this work is in the frame
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Bibliographic record
Abstract
We provide a game theoretic formulation for a sensor activation problem in a multi-attribute environment. Activated sensors randomly select one of M environmental attributes, and transmit data on that attribute to an end user. The goal is to maximize the number of attributes reported while minimizing redundant reports and packet collisions, which both increase with the number of active sensors. Sensor participation is optimized according to an adaptive scheme, in which sensors activate only when their expected utility, given by the number of unique attributes reported minus an energy cost, is positive. We formulate a Nash equilibrium policy that maximizes the expected performance from the perspective of each sensor when transmission is according to a one-shot frequency hopping scheme, and compare this to the global optimum
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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 | 0.000 | 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