Distributed Clustering Techniques for Improving Lifetime in Two-Tiered Sensor Networks
Bibliographic record
Abstract
Summary form only given. In hierarchical two-tiered sensor networks, higher-powered relay nodes have recently been proposed to be used as cluster heads for designing scalable sensor networks.The assignment of sensor nodes to clusters in an energy-efficient way is known to improve the lifetime of such networks. In this paper we have proposed two efficient distributed algorithms for assigning sensor nodes to clusters in two-tiered networks. The first heuristic assumes that all relay nodes, acting as cluster heads, send their data directly to the base station. The second heuristic relaxes this assumption and is to be used with any network where each relay node uses a multi-hop route to send its data to the base station. Simulations on networks of different sizes show that our approaches consistently outperform existing heuristics for clustering in two-tier sensor networks and are fast enough to be used for practical networks containing hundreds of sensor nodes. We have compared the results of our distributed approaches with the optimal solutions obtained using an existing approach based on an integer linear program (ILP) formulation and have shown that, on an average, our approaches, with a relatively small overhead, can produce results that are close to the optimal solutions.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".