Optimal Load Balanced Clustering in Two-Tiered Sensor Networks
Bibliographic record
Abstract
In hierarchical sensor networks, sensor nodes are arranged in clusters, and higher-powered relay nodes can be used as cluster heads. The scalability and the lifetime of sensor networks are affected by the limited transmission range and battery power of the nodes. Proper techniques for assigning sensor nodes to clusters have been shown to improve the lifetime of the network. Previous approaches to clustering focus on different heuristics to achieve load balancing. In this paper, we have proposed two fast and efficient integer linear program (ILP) formulations for assigning sensor nodes to clusters in a two-tiered network, where the relay nodes are used as cluster heads. The first is for single hop routing and the second is a generalized formulation that can be used with any multi-hop routing strategy. The objective, in both cases, is to maximize the lifetime of the relay node network. We have tested our formulations with a number of different routing strategies, and for each case, we have compared our formulation to several existing heuristics for clustering. The results demonstrate that our ILP's consistently outperform the heuristics and are fast enough to be used for practical 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".