Energy Aware Distributed Clustering in Two-Tiered Sensor Networks
Bibliographic record
Abstract
Two-tiered sensor networks, where higher-powered relay nodes are used as cluster heads, have been proposed recently for designing sensor networks. Assigning sensor nodes to clusters, in an energy efficient way, is known to improve the lifetime of such networks. In this paper we have proposed an efficient distributed algorithm for assigning sensor nodes to clusters in two-tiered networks, using both single-hop and multi- hop routing schemes. Our distributed clustering strategy allocates sensor nodes to clusters, based on limited local information only. However, the solutions generated are shown to be comparable to optimal solutions obtained using an ILP formulation. We have also compared our approach to a number of existing heuristics recently proposed in the literature and have shown, through simulations, that our approach consistently outperforms current heuristics. In summary, the quality of the solutions obtained using our approach is comparable to those obtained using an ILP formulation, but the solutions can be generated very quickly, making it suitable for practical-sized networks with hundreds of sensor nodes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".