Connectivity optimization with realistic lifetime constraints for node placement in environmental monitoring
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
Maximizing network connectivity while maintaining a useful period of lifetime is a challenging design objective for wireless sensor networks (WSNs). Satisfying such objective becomes an even more intricate task in harsh operational environments such as those found in forestry applications. While much work has been presented aimed at forestry applications, only a few have addressed the unique characteristics of forestry settings, such as 3-D deployment and operational requirements. In this paper, we introduce a novel deployment strategy for relay nodes in WSNs for forestry applications. The strategy optimizes network connectivity, while guarantying specific network lifetime. Key to our contribution is a revised definition for network lifetime that is more realistic and more fitting to forestry applications. The effectiveness of our strategy is validated through extensive simulation and comparisons.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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