Effect of fading on the <i>k</i>‐coverage of wireless sensor networks
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".