Distributed Coverage Control of Mobile Sensor Networks Subject to Measurement Error
Bibliographic record
Abstract
Deployment algorithms proposed to improve coverage in sensor networks often rely on the Voronoi diagram, which is obtained by using the position information of the sensors. It is usually assumed that all measurements are sufficiently accurate, while in a practical setting, even a small measurement error may lead to significant degradation in the coverage performance. This paper investigates the effect of measurement error on the performance of coverage control in mobile sensor networks. It also presents a distributed deployment strategy, namely the Robust Max-Area strategy, which uses information on error bounds in order to move the sensors to appropriate locations. To this end, two polygons are obtained for each sensor, and it is shown that the exact Voronoi polygon (associated with accurate measurements) lies between them. A local spatial probability function is then derived for each sensor, which translates the available information about the error bound into the likelihood of the points being inside the exact Voronoi polygon. Subsequently, the deployment strategy positions each sensor such that the total covered area increases. The sensors' movements are shown to be convergent under the proposed strategy.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".