Efficient Coverage Planning for Grid-Based Wireless Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we study efficient triangular grid-based sensor deployment planning for coverage when sensor placements are perturbed by random errors around their corresponding grid vertices, where the random errors are modeled by uniform displacements inside error disks of a given finite radius. The average coverage percentage of the sensing field is derived as a function of the length of the grid tiles d, and the radius of the random error disks, R. Our expressions for the average coverage percentage are computed numerically and verified by Monte-Carlo simulations. The analytical methods can be used with other types of grid-based deployment with little modification, such as square grid-based deployment. One appealing feature of grid-based deployment that we observe is that the sensing coverage is rather resilient to random errors. Based on this observation and the quantitative results from our analysis, we discuss several approaches to efficient grid-based deployment planning for coverage and illustrate these through numerical examples.
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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 | 0.001 |
| 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 it