intel-LEACH: An optimal framework for node selection using dynamic clustering for wireless sensor networks
Bibliographic record
Abstract
Over the last decade, the field of Wireless Sensor Networks (WSNs) has made expansive strides in the area of Radio Communication Systems. A vast majority of these systems are deployed in failure-prone environments which results in chronic communication losses due to unreliable wireless connections, malicious attacks and resource-constrained features. Hence WSNs necessitate adaptive protocol frameworks applicable for differing network densities either as sparse or denser deployments. One of the major challenges in WSNs is to achieve an optimal trade-off between data precision and energy efficiency of a sensor node from the perspective of network longevity. In this article, we propose an optimized protocol intel-LEACH for selecting high yield nodes among randomly deployed sensor nodes based on dynamic optimization strategy. Our proposed model selects a node that can guarantee higher data precision and has maximal residual energy to serve as a cluster head (CH). Meanwhile, the others sensor nodes apart from CHs must also exhibit capability in terms of data transmission accuracy to join a given cluster irrespective of the distance between the sensor nodes and CHs. Thus our model minimizes loss of high yield nodes for every round and overall WSN performance is boosted by its network lifetime extension.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".