Mobile Sensors Deployment Subject to Location Estimation Error
Bibliographic record
Abstract
Voronoi-based mobile sensor deployment algorithms require the knowledge of sensors' locations to guarantee a simple reliable coverage detection, and they miss the mark if the location is inaccurate. However, in practice, it is often too expensive to include a Global Positioning System (GPS) receiver in each node, and location information is inaccurate as sensors estimate locations from the messages they receive. We study sensor deployment algorithms in the presence of location estimation error for sensors with nonidentical sensing ranges. We propose a set of Voronoi-based diagrams, which are called guaranteed Voronoi diagrams (VDs), that guarantee single-cell-based coverage hole detection algorithms, provided that upper bounds on localization errors are assumed. Although inaccuracy of location information would appear to deteriorate the total coverage, our simulation results demonstrate that the proposed algorithms can exploit this inaccuracy to improve network coverage. Hence, even if the location information is exactly known at each node, assuming some error margins improves the network coverage if guaranteed Voronoi diagrams are used.
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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 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".