An MDP-Based Dynamic Optimization Methodology for Wireless Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
Wireless sensor networks (WSNs) are distributed systems that have proliferated across diverse application domains (e.g., security/defense, health care, etc.). One commonality across all WSN domains is the need to meet application requirements (i.e., lifetime, responsiveness, etc.) through domain specific sensor node design. Techniques such as sensor node parameter tuning enable WSN designers to specialize tunable parameters (i.e., processor voltage and frequency, sensing frequency, etc.) to meet these application requirements. However, given WSN domain diversity, varying environmental situations (stimuli), and sensor node complexity, sensor node parameter tuning is a very challenging task. In this paper, we propose an automated Markov Decision Process (MDP)-based methodology to prescribe optimal sensor node operation (selection of values for tunable parameters such as processor voltage, processor frequency, and sensing frequency) to meet application requirements and adapt to changing environmental stimuli. Numerical results confirm the optimality of our proposed methodology and reveal that our methodology more closely meets application requirements compared to other feasible policies.
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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