Modeling of Data Reduction in Wireless Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we present a stochastic model for data in a Wireless Sensor Network (WSN) using random field theory. The model captures the space-time behavior of the underlying phenomenon being observed by the network. We present results regarding the size and spatial distribution of the regions of the network that sense statistically extreme values of the underlying phenomenon using the theory of extreme excursion regions. These results compliment many existing works in the literature that describe algorithms to reduce the data load, but lack an analytical approach to evaluate the size and spatial distribution of this load. We show that if only the statistically extreme data is transmitted in the network, then the data load can be significantly reduced. Finally, a simple performance model of a WSN is developed based on a collection of asynchronous M/M/1 servers that work in parallel. We derive several performance measures from this performance model. The presented results will be useful in the design of large scale sensor networks.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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