Optimization of 3D WSN coverage based on equilibrium optimization algorithm
Bibliographic record
Abstract
Coverage optimization is one of the basic problems in wireless sensor networks. Coverage reflects the service quality provided by wireless sensor networks. Swarm intelligence algorithm is an optimization method inspired by natural organisms. Node coverage optimization is also an optimization problem. Swarm intelligence algorithm can solve the coverage problem of wireless sensor networks. Therefore, this paper focuses on the application of swarm intelligence algorithm in coverage optimization of wireless sensor networks, and proposes a coverage optimization strategy based on swarm intelligence algorithm: coverage optimization of three-dimensional wireless sensor networks based on equilibrium optimization algorithm. In this algorithm, the principle is to control the volume and mass balance model, the particle concentration update according to the equilibrium candidate solution, and finally reach the equilibrium state, which mainly consists of three stages: population initialization, equilibrium pool and concentration update. In the simulation, the equilibrium optimization algorithm has higher effective coverage than the particle swarm optimization algorithm.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".