Randomized k-Coverage Algorithms For Dense Sensor Networks
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Bibliographic record
Abstract
We propose new algorithms to achieve <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -coverage in dense sensor networks. In such networks, covering sensor locations approximates covering the whole area. However, it has been shown before that selecting the minimum set of sensors to activate from an already deployed set of sensors is NP-hard. We propose an efficient approximation algorithm which achieves a solution of size within a logarithmic factor of the optimal. We prove that our algorithm is correct and analyze its complexity. We implement our algorithm and compare it against two others in the literature. Our results show that the logarithmic factor is only a worst-case upper bound and the solution size is close to the optimal in most cases. A key feature of our algorithm is that it can be implemented in a distributed manner with local information and low message complexity. We design and implement a fully distributed version of our algorithm. Our distributed algorithm does not require that sensors know their locations. Comparison with two other distributed algorithms in the literature indicates that our algorithm: (i) converges much faster than the others, (ii) activates near-optimal number of sensors, and (iii) significantly prolongs (almost doubles) the network lifetime because it consumes much less energy than the other algorithms.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it