E-BACH: Entropy-Based Clustering Hierarchy for Wireless Sensor Networks
Bibliographic record
Abstract
Environmental wireless sensor networks are usually composed of a large group of cooperating wireless sensor nodes spread over an area of interest. They often operate in remote locations and thus must be designed for energy-efficiency and reliability. At the same time, their deployment and operational characteristics must correspond to the spatial distribution of the phenomena of interest. There are a number of approaches that attempt to resolve this trade-off between energy consumption and quality of collected data. Most of them concentrate directly on energy efficiency and communication aspects of network operation. In this contribution, we propose a new method that uses data quality as the primary goal of network optimization. Based on the concept of data entropy, it develops a hierarchical heterogeneous network where individual nodes sample the measured quantities according to the potential information gain. This can be used to save energy by lowering sampling rates of nodes at location with low variability of monitored variables or high correlation with other nodes.
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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 | 0.001 | 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 | 0.001 | 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".