Architecture of Wireless Sensor Networks With Mobile Sinks: Sparsely Deployed Sensors
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Bibliographic record
Abstract
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this paper, we propose to develop wireless Sensor Networks with Mobile Sinks (MSSNs). The proposed MSSN is highly energy efficient, because the multihop transmissions of high-volume data over the network are converted into single-hop transmissions. We focus our investigation on sparsely deployed networks, where single node-to-sink transmission is considered. The transmission-scheduling algorithm (TSA-MSSN) is proposed, where a parameter <formula formulatype="inline"><tex>$\lambda$</tex></formula> is employed to control the tradeoff between the maximization of the probability of successful information retrieval and the minimization of the energy-consumption cost. It is shown that the proposed implementation of the TSA-MSSN has a complexity of <formula formulatype="inline"> <tex>$O$</tex></formula>(1). This paper serves as the foundation for understanding fundamental laws behind the aforementioned tradeoff with useful implications for the design of more complex MSSNs. </para>
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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