Efficient Energy-Aware Clustering Approach Area Splitting-Based for Wireless Sensor Networks
Bibliographic record
Abstract
Modern wireless communications advancements and miniaturization components in the field of electronics have led to the development of lower-cost, multi-functional microsensors.A very new useful type of network, called Wireless Sensor Networks (WSNs), has emerged in recent decades, acting as generators and relay data by controlling interesting physical phenomenon.However, some problems need to be improved in their mastery, particularly that of energy conservation.This valuable energy has to be sufficiently available until a WSN reaches its mission.In order to increase WSN lifetime, numerous protocols have been designed to address this problem of power consumption.The pioneer of these protocols was Low Energy Adaptive Clustering Hierarchy (LEACH) protocol and other enhancement variants were subsequently created in this context.In this paper, a new protocol named Area Splitting for Clustering (ASC) that reduces energy consumption and increases network lifetime is proposed.The performance evaluation of ASC has been carried out and showed that it outperforms those of Leach and its variant protocols.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".