Minimum cost guaranteed lifetime design for heterogeneous wireless sensor networks (WSNs)
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
Node placement strategy is an intrinsic issue when provisioning of a wireless sensor network (WSN). In this paper, we address the placement problem for a class of heterogeneous WSNs, wherein nodes have different energy supplies and functionalities. We formulate a generalized node placement optimization problem aiming at minimizing the network cost with constraints on lifetime and connectivity. We propose a placement scheme with two phases. We model the placement of the first phase relaying nodes (FPRNs) as a minimum set cover problem and a dynamic programming algorithm is developed to solve it. For the placement of the second phase relaying nodes (SPRNs), we derive two fundamental design principles-the far-near strategy and max-min strategy. The implementation of the placement schemes is illustrated by examples. Our proposed mechanism is a first attempt towards facilitating realistic relay node placement in WSNs.
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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 | 0.001 | 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 | 0.002 | 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