Layered Clustering for Solar Powered Wireless Visual Sensor Networks
Bibliographic record
Abstract
Visual-based wireless sensor networks have been implemented in several different fields such as environment monitoring, military applications, and robotic applications. Due to the limitation of node's specification, the bandwidth and energy become critical issues for sensor nodes. In this paper, we employ a solar cell recharging model and a layered clustering model to deal with the restrict energy consumption under the consideration of visual quality. The system lifetime can be prolonged by rechargeable solar cell that can be recharged by solar panel in daytime. In addition, we analyze the simulation results of energy consumption and total transmitted packets by changing the aggregation rate and gate energy (GE). With the aggregation rate decreasing, the cluster head in inner layer can support more visual nodes and reserve more bandwidth. The lower GE can reduce the packets loss during the system charging process. The analysis and experiment result obtained in this paper prove that with the combination of layered clustering and solar recharging, the performance of wireless visual sensor network can be enhanced under the consideration of the restrict node's capacity and video distortion.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".