Deployment algorithms for coverage improvement in a network of mobile sensors with measurement error in the presence of obstacles
Bibliographic record
Abstract
In this work, a novel Voronoi-based diagram is introduced which assigns a distinct region to each sensor in the presence of obstacles such that the regions are mutually disjoint and if one sensor cannot cover a point inside its region, no other sensor can detect it either. The proposed diagram called obstructed guaranteed additively weighted (OG/\ W) Voronoi diagram is the main tool for developing the sensor deployment algorithms in a network of mobile sensors with nonidentical sensing ranges in the presence of obstacles. Then, two algorithms are developed to improve the prioritized coverage when the exact location of sensors is not available due to measurement error. The developed algorithms are iterative, and in each iteration each sensor calculates its new location based on the value of a priority function of the points inside the corresponding OGAW Voronoi region and moves toward it such that the overall weighted coverage of the network is increased. Simulation results confirm the effectiveness of the developed algorithms.
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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 | 0.002 | 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".