An Interoperable Coverage Model for Field Sensor Networks Deployment
Bibliographic record
Abstract
Recent advances in sensor technology have enabled the implementation of more efficient and cost-effective sensor networks for many applications, such as surveillance and monitoring. The major challenge in these applications is developing an effective and comprehensive performance measure to consider sensor and environment parameters. The nature of spatial distribution for field sensors incurs a lot of difficulties for such development and, hence, poses an intriguing research problem. This paper addresses the field sensor network (FSN) deployment problem for area coverage. It presents the theoretical foundations for developing a sensor-agnostic coverage model that enables the interoperability of coverage models across heterogeneous FSN deployment for 2D/3D environments. In particular, a performance measure is developed that characterizes the closeness between a single field sensor and a target configuration by transforming the effective sensing region of any field sensor transforms into a closed n-ball (for n=2,3) under a homeomorphism mapping. By utilizing the proposed performance measure, a non-Euclidean variant of Voronoi partitioning is implemented, which enables distributed gradient-based optimization for FSN deployment to achieve optimal coverage. The comparative simulations and evaluation of the results show the effectiveness of the proposed approach.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".