Coverage protocols for detecting fully sponsored sensors in wireless sensor networks
Bibliographic record
Abstract
Sensing coverage preserving is a hot research spot in the wireless sensor network. In order to simplify the research on such issue, the disk sensing range assumption is used in most coverage-aware algorithms. Based on such assumption some efficient central angle methods were proposed to identify fully sponsored sensors. However, the disk assumption is too strong in the real world and can not be held in high accurate scenarios. This paper investigates the coverage problem under both disk and simple polygon sensing range assumptions. An Association Sponsors Method (ASM) was described in order to enhance the central angle method while a new Intersection Point Method (IPM) were proposed for simple polygon sensing range. In order to provide an adjustable accuracy we devise an Unit Circle Test(UCT) method which can satisfy different accuracy requirements by adjusting test radius to tolerant holes. Our protocols were implemented on in the NS-2 simulator. Performance of our schemes were evaluated through a set of simulation experiments and compared to the Central Angle Method (CAM). Our protocols can efficiently identify fully covered sensors, discover holes (blind points), and archieve better quality results than CAM under both disk sensing range and simple polygon sensing range assumption. The performance and flexibility of IPM makes it a potential solution for applications that require a high coverage accuracy
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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 | 0.001 | 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".