HDRE: Coverage hole detection with residual energy in wireless sensor networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Coverage completeness is an important indicator for quality of service in wireless sensor networks (WSN). Due to limited energy and diverse working conditions, the sensor nodes have different lifetimes which often cause network holes. Most of the existing methods expose large limitation and one-sidedness because they generally consider only one aspect, either coverage rate or energy issue. This paper presents a novel method for coverage hole detection with residual energy in randomly deployed wireless sensor networks. By calculating the life expectancy of working nodes through residual energy, we make a trade-off between network repair cost and energy waste. The working nodes with short lifetime are screened out according to a proper ratio. After that, the locations of coverage holes can be determined by calculating the joint coverage probability and the evaluation criteria. Simulation result shows that compared to those traditional algorithms without consideration of energy problem, our method can effectively maintain the coverage quality of repaired WSN while enhancing the life span of WSN at the same time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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