Cluster-Based Correlated Data Gathering in Wireless Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
We consider the problem of optimal cluster-based data gathering in Wireless Sensor Networks (WSNs) when nearby readings are spatially correlated. Due to the dense nature of WSNs, data samples taken from nearby locations are statistically similar. We show how this data correlation can be exploited to reduce the amount of data to be transmitted in the network and thus conserve energy. While much attention in recent years has been paid to analyzing and optimizing cluster-based WSNs from various perspectives, the problem of energy-efficient clustering of WSNs in presence of data correlation is not yet fully explored. In this paper, we model a single-cluster network and analytically characterize the optimal cluster size subject to its distance from the sink as well as the degree of correlation. Contrary to existing approaches, our findings show that heterogeneous-sized clusters, where the clusters further from the sink are larger, are more energy-efficient. We also propose a heuristic greedy clustering algorithm to find a near-optimal solution to the problem of energy-efficient clustering. Simulation results confirm the effectiveness of having heterogeneous-sized clusters in WSNs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 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 it