Efficient k-Coverage algorithms for wireless sensor networks and their applications to early detection of forest fires
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Achieving k-coverage in wireless sensor networks has been shown before to be NP-hard. We propose an efficient approximation algorithm which achieves a solution of size within a logarithmic factor of the optimal. 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. Simulation results show that our distributed algorithm converges faster and consumes much less energy than previous algorithms. We use our algorithms in designing a wireless sensor network for early detection of forest fires. Our design is based on the Fire Weather Index (FWI) System developed by the Canadian Forest Service. Our experimental results show the efficiency and accuracy of the proposed system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it