Power-Efficient Clustering in Wireless Sensor Networks under Coverage Constraint
Bibliographic record
Abstract
Maximizing network lifetime and minimizing energy consumption and are two central issues in the design wireless sensor networks (WSN) protocols. In this paper, we address energy-efficient state assignment to sensors in cluster-based WSN, under the constraint of full coverage of the area the sensors are deployed in and connectivity of cluster heads. We consider that any sensor can be turned on, turned off or promoted cluster head, each of these states having a predefined power consumption level.We propose a sensor state assignment heuristic that processes an energy-efficient sensor configuration where every sensor is connected to a cluster head. Besides, we constraint any admissible configuration to have all its cluster heads forming a spanning tree used as a logical routing topology. First, we formulate this global problem as an Integer Linear Programming model that we prove NP-Complete. Then, we implement a greedy heuristic and we show that, compared to its lower bound, this heuristic provides quite good network lifetime values while performing low computation times, practically suitable for large-sized sensor networks.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".