An Energy-Efficient Unequal Clustering Routing Algorithm for Wireless Sensor Network
Bibliographic record
Abstract
The existing clustering routing protocols face imbalanced energy consumption and the hotspot problem.To solve the problems, this paper proposes an energy-efficient unequal clustering routing algorithm (UCRA).Firstly, the monitoring area was divided by concentric circles into rings of different sizes.Next, the cluster heads were elected based on position and residual energy.Before clustering, each common sensor joins a cluster based on the electability of each cluster head.The electability is defined based on the residual energy of the cluster head and the distance from the cluster head to the centerline of the ring of the common sensor.For multi-hop routing from a cluster head to the base station, the routing sensor was selected dynamically based on the local ring of the cluster head and the residual energy of neighboring cluster heads.The simulation results show that the UCRA can effectively solve the hot-spot problem in uniform clustering routing protocols, balance the energy consumption of network sensors, and extend the network lifecycle.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".