Data Analysis in Wireless Sensor Networks with Distributed Self Organizing Map
Bibliographic record
Abstract
Distributed clustering algorithms are employed in wireless sensor network (WSN) to improve the local data analysis. This process is carried out collaboratively with the help of nearby neighbours without a central controller. In this paper, distributed clustering is performed with Self Organizing Map (SOM). The SOM is a popular unsupervised neural network model that maps input data to a lower-dimensional grid. On this grid map similar input patterns are placed closer to each other. This process helps in discovering patterns and relationships in the data without prior labeling, thus making proposed Distributed Self Organizing Map (DSOM) useful for unknown local data analysis at the WSNs. The proposed algorithm is applied to analyze two real life WSN datasets: Water quality monitoring of Thames river, Weather monitoring dataset of various stations at Canada. Comparative analysis is carried out with Distributed Particle Swarm optimization algorithm and Distributed K-means algorithm. The proposed DSOM has superior performance, as indicated by Silhouette Index and Quantization Error measurements.
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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 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".