Effect of fading on the <i>k</i>‐coverage of wireless sensor networks
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Bibliographic record
Abstract
Abstract In a Wireless Sensor Network (WSN), coverage performance of the network is affected by multiple factors such as source power, sensitivity of sensors, and the quality of the channel between the source and the sensors. In most existing works on WSNs, only path‐loss of the wireless channel is considered, that with further assumption of absence of obstacles in the sensing region of a sensor, results in circular‐type of coverage for each node. However, in some WSN applications, the channel is not line‐of‐sight and exhibits multipath fading. In this paper, effect of the multipath fading on k ‐coverage of randomly deployed WSNs is analytically investigated via techniques from stochastic geometry. More specifically, the k ‐coverage probability is analytically derived under Rayleigh, Rician, and Nakagami fading assumptions. Numerical results are also presented to compare the derived k ‐coverage probability with the commonly used k ‐coverage models that do not consider the fading effect. These results reveal the level of the k ‐coverage degradation due to multipath fading compared to the case of no fading (fixed range), which in some cases is shown to be very significant.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 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