Wireless Sensor Networks: To Cluster or Not To Cluster?
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
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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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it