A Probabilistic Coverage Protocol for Wireless Sensor Networks
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Bibliographic record
Abstract
We propose a new probabilistic coverage protocol (denoted by PCP) that considers probabilistic sensing models. PCP is fairly general and can be used with different sensing models. In particular, PCP requires the computation of a single parameter from the adopted sensing model, while everything else remains the same. We show how this parameter can be derived in general, and we actually do the calculations for two example sensing models: (i) the probabilistic exponential sensing model, and (ii) the commonly-used deterministic disk sensing model. The first model is chosen because it is conservative in terms of estimating sensing capacity, and it has been used before in another probabilistic coverage protocol, which enables us to conduct a fair comparison. Because it is conservative, the exponential sensing model can be used as a first approximation for many other sensing models. The second model is chosen to show that our protocol can easily function as a deterministic coverage protocol. In this case, we compare our protocol against two recent deterministic protocols that were shown to outperform others in the literature. Our comparisons indicate that our protocol outperforms all other protocols in several aspects, including number of activated sensors and total energy consumed. We also demonstrate the robustness of our protocol against random node failures, node location inaccuracy, and imperfect time synchronization.
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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 | 0.001 | 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