A performance evaluation of a coverage compensation based algorithm for wireless sensor networks
Bibliographic record
Abstract
Recent years, coverage has been widely investigated as one of the fundamental quality measurements of wireless sensor networks. In order to maintaining the coverage while saving energy of networks, algorithms have been developed to keep a minimum cover set of sensors working and turn off the redundant sensors. Generally, centralized algorithms can give a better result than distributed algorithms in terms of the number of active sensors. However, the heavy computation requirements and message overhead for collecting geographical location data keep centralized algorithms out of most distributed scenarios. In this article, Based on the idea of coverage compensation a distributed node partition algorithm for random deployments is presented to generate a minimum cover set by using the optimal node distributions created by the centralized algorithms such as GA. A Genetic Algorithm for coverage is proposed too to demonstrate how an optimal coverage node distribution created by GA can be used in a distributed scenario. Ours works are simulated on JGAP and NS2. The simulation result shows that our partition algorithm based on coverage compensation can achieve the same performance as OCOPS in terms of coverage and number of active sensors while using less control messages.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".