Near-Optimal Node Clustering in Wireless Sensor Networks for Environment Monitoring
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
Wireless sensor networks (WSNs) for environment monitoring consist of a large number of low-cost battery-powered sensors nodes, densely deployed throughout a remote or inaccessible physical space. "Energy conservation" is identified as the key challenge in the design and operation of these networks. In our earlier work, we prove that WSN clustering schemes capable of positioning their resultant clusters within the isoclusters of the monitored phenomenon have the potential to reduced the nodes' energy consumption and, thereby, prolong the network lifetime. However, a careful analysis of the existing WSN clustering algorithms shows that these algorithms do not consider the similarity of sensed data as a clustering criterion, and therefore cannot provide optimal performance in terms of energy conservation. In this paper, a novel clustering algorithm, local negotiated clustering algorithm (LNCA), which employs the similarity of nodes' readings as an important criterion in cluster formation, is presented. LNCA greatly reduces the data-reporting related traffic with reasonable clustering cost. Simulations show that LNCA achieves considerable improvements over the most popular WSN clustering algorithm-low-energy adaptive clustering hierarchy (LEACH)
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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 | 0.000 | 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 | 0.001 | 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 it