A lightweight dynamic optimization methodology for wireless sensor networks
Bibliographic record
Abstract
Technological advancements in embedded systems due to Moore's law have lead to the proliferation of wireless sensor networks (WSNs) in different application domains (e.g. defense, health care, surveillance systems) with different application requirements (e.g. lifetime, reliability). Many commercial-off-the-shelf (COTS) sensor nodes can be specialized to meet these requirements using tunable parameters (e.g. voltage, frequency) to specialize the operating state. Since a sensor node's performance depends greatly on environmental stimuli, dynamic optimizations enable sensor nodes to automatically determine their operating state in-situ. However, dynamic optimization methodology development given a large design space and resource constraints (memory and computational) is a very challenging task. In this paper, we propose a lightweight dynamic optimization methodology that intelligently selects initial tunable parameter values to produce a high-quality initial operating state in one-shot for time-critical or highly constrained applications. Further operating state improvements are made using an efficient greedy exploration algorithm, achieving optimal or near-optimal operating states while exploring only 0.04% of the design space on average.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".