Near-Optimal Node Clustering in Wireless Sensor Networks for Environment Monitoring
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 source (direct Gemma or distilled Codex), 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".