Genetic algorithm for data aggregation trees 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
aggregation trees. This paper uses a genetic algorithm (GA) to generate balanced and energy efficient data aggregation spanning trees for wireless sensor networks. In a data gathering round, a single best tree consumes lowest energy from all nodes but assigns more load to some sensors. As a result, the energy resources of heavily loaded nodes will be depleted earlier than others. Therefore, we use a collection of trees that balances load among nodes and consumes less energy. Our proposed GA takes these two issues in generating aggregation trees. The GA is simulated in an open source simulator, J-sim. The simulation results show that proposed GA outperforms a few other data aggregation tree-based approaches in terms of extending network lifetime. 1
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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 | 0.002 | 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 it