An Energy-Efficient Clustering Approach for Wireless Sensor Networks to Reduce Hot-Spot Effect and Idle Listening Energy Consumption
Bibliographic record
Abstract
Nowadays, wireless sensor networks (WSNs) prove their potential in our daily day-today life.However, due to high congestion, energy management becomes the key challenge for WSNs.To increase the lifespan of WSNs, a unique clustered routing strategy is presented in this study.It offers an effective solution for the hot-spot effect and idle-listening issues.Outcomes help in lessening energy consumption.The developed algorithm is based on the principle of balanced energy consumption.Further, the developed WSN involves a node dormancy mechanism.It requires the energy balance technique using the clustering routing mechanism with distance variance.The design of clustering nodes is based on the master-slave principle, where the formation of clustering relies on node position and residual energy.MATLAB provides the simulation results as energy drop of each node to calculate the battery life.According to the achieved results, the developed algorithm can reduce the decay rate which can further lessen the energy consumption of the network.Moreover, it enhances the throughput and prolongs the network lifetime.The paper provides an energy-efficient clustering approach for Wireless Sensor Networks (WSNs) that can directly relate to manufacturing applications by practical solutions to the challenges faced in manufacturing settings, where effective sensor network deployment can lead to significant improvements in production processes and overall operational efficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".