Regression-Based Law of Energy Efficiency in Wireless Sensor Networks
Bibliographic record
Abstract
Wireless Sensor Networks play a pivotal role in various applications, ranging from environmental monitoring to industrial automation. Efficient clustering of these network nodes is crucial for optimizing communication and energy consumption. In this study, we compare the performance of two clustering methods, namely k-means and grid-based clustering, in terms of energy efficiency. The nodes are assumed to be uniformly distributed across a two-dimensional area, and clusters are formed using the k-means algorithm. A baseline model is established using grid clustering for comparison purposes. To evaluate the efficiency of these clustering approaches, the energy consumption analysis is based on Friss law, and regression algorithms are employed to analyze energy consumption patterns within the network. Through regression analysis, an energy efficiency law is determined for all the cases analyzed. Our results underscore an approach to optimize energy consumption by formulating a functional relationship for total transmission power, derived from regression analyses conducted across diverse simulation scenarios.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".