Bibliographic record
Abstract
The key challenge in the design and operation of wireless sensor networks (WSNs) is the maximization of system lifetime. Node clustering is commonly considered as one of the most promising techniques for dealing with the given challenge, and as such has been referred to by many researchers. It is interesting to observe, however, that very few, if any, published research works provide explicit analysis of node clustering in WSNs and/or manage to prove its actual effectiveness. In this paper we take a closer analytical look at WSNs of clustered organization. We prove that these networks do not necessarily outperform non-clustered WSNs. The condition that ensures superior performance of clustered WSNs, with absolute certainty, is that the formed clusters lie within the isoclusters of the monitored phenomenon. We also show that in clustered WSNs which satisfy the given condition, cluster sizes do not need to match the sizes of their respective underlying isoclusters. Instead, simple 5-hop clusters can provide near-optimal network performance under a wide range of cluster-to-sink and cluster-to-isocluster spatial arrangements
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".