Maximum WSN coverage in environments of heterogeneous path loss
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
We study wireless sensor network (WSN) node placement in an environment where RF signal losses vary with position. This reflects real–world outdoor environments where vegetation and topography cause nonuniform path loss. Many techniques that solve for a variety of objective functions subject to various constraints have previously been proposed for node placement. However, many of these methods make simplifying assumptions such as all nodes having the same transmission range. Our goal is to take the insights and approaches of this previous ork and extend it to real–world environments. The present work assumes we have a map that quantifies the path loss behaviour of the real environment. Based on this map, and a path loss model that accounts for spatial variations in the path loss exponent, we propose a node placement algorithm for two–tiered WSNs that maximises the area covered by a specified number of relay nodes and sensor nodes.
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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 | 0.001 | 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 | 0.001 | 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