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" has been identified as the key challenge in the design and operation of these networks. At the same time, clustering of sensor nodes has been widely recognized as the most promising approach in dealing with the given challenge. In our earlier work, we examine the actual energy-conservation effectiveness of node clustering in WSNs, and we prove that only clustering schemes that position their resultant clusters within the isoclusters1 of the monitored phenomenon are guaranteed to reduce the nodes' energy consumption and extend the network lifetime. A thorough review of the known literature on WSNs shows that the existing WSN clustering algorithms commonly do not satisfy the above requirement, i.e. they do not consider the similarity of sensed data as an important clustering criterion. Therefore, the utilization of these algorithms cannot be considered truly effective in dealing with the WSN energy conservation challenge. In this paper, we propose a novel WSN clustering algorithm - local negotiated clustering algorithm (LNCA). To our knowledge, LNCA is the first clustering algorithm that employs the similarity of nodes' readings as the main criterion in cluster formation. As such, LNCA is highly effective in minimizing in-network data-reporting traffic and, accordingly, in reducing the energy usage of individual sensor nodes. Our simulation results show clear performance supremacy of LNCA over two popular WSN clustering algorithms: low-energy adaptive clustering hierarchy (LEACH) and weight clustering algorithm (WCA).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".