Efficient Cluster Head Selection in Wireless Sensor Networks Using Heap Tree-Based Optimization
Bibliographic record
Abstract
Improving Wireless Sensor Network (WSNs) performance and energy efficiency depends critically on CH (Cluster Head) selection. Choosing a CH decides network lifetime, scalability, and reliability, thus is an important part of WSN protocols. We propose a new cluster head selection approach using a Heap Tree-based optimization technique that sorts the nodes based on the important parameters like residual energy, distance from base station and connectivity. Using a dynamic priority function that ranks these nodes, the method builds a heap tree to allow efficient and scalable selection of CH. The Heap Tree-based CH selection outperforms the traditional algorithms, such as LEACH, TEEN, HEED in energy consumption reduction and increasing network lifetime. Simulation findings demonstrate that the suggested approach can guarantee dependability of data transfer and equally distribute energy across nodes. We examine the performance of the suggested algorithm under various networks conditions and show through simulation that its provides better energy efficiency and longer network life.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".