Distributed coverage optimization in a network of mobile agents subject to measurement error
Bibliographic record
Abstract
The effect of localization error in mobile sensor networks is investigated in this paper. Localization is an essential building block in mobile sensor networks, and is achieved through information exchange among the sensors. Sensor deployment algorithms often rely on the Voronoi diagram, which is obtained by using the position information of the neighboring sensors. In the sensor network coverage problem, it is desired to place each sensor in a proper position in its Voronoi cell such that its local coverage increases. On the other hand, it is often assumed that all measurements are sufficiently accurate, while in a practical setup even a small localization error may lead to significant uncertainty in the resultant Voronoi diagram. This paper is concerned with the degrading effect of position measurement error in the sensor network coverage problem. To this end, the effect of localization error on the boundaries of the Voronoi polygons is investigated. Two polygons are obtained for each sensor, and it is shown that the exact Voronoi polygon (corresponding to accurate localization) lies between them. The area between these two polygons is directly related to the size of error. A sensor deployment strategy is presented based on these two polygons, using a quantitative local density function which takes the uncertainty of the Voronoi polygons into account to maximize the local coverage of each sensor.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".