Node Position Estimation and Coverage Hole Detection in Wireless Sensor Networks Using Clustering-Guided Twin Contrastive Learning with Meerkat Optimization Algorithm
Bibliographic record
Abstract
Multiple deployment scenarios exist where Wireless Sensor Networks (WSNs) serve as important elements for environmental surveillance and emergency response and medical infrastructure monitoring. Traditional node position estimating approaches along with coverage hole detection instruments make networks unreliable in performance terms. GPS-based localization methods demonstrate shortcomings due to high resource allocation and limited benefits for battery-constrained sensor nodes. Traditional coverage hole detection methods that use static models show poor performance during network dynamics because node failures alongside mobility result in reduced monitoring accuracy. The investigation leads to the development of a novel framework that unites optimization approaches with machine learning algorithms to handle present challenges. With the Crayfish Optimization Algorithm the exact positioning of nodes enables the precise detection of edge nodes throughout the network. Through a Clustering-Guided Twin Contrastive Learning (CG-TCL) protocol network voids become detectable through the discovery process which processes network spatial and contextual behavior patterns. After MOA optimization of CG-TCL models practitioners experience both enhanced calculation speed along with improved predictive accuracy. The framework achieves superior detection of node positions along with coverage holes while maintaining 99.9% precise performance to enhance both WSN reliability and scalability and environmental adaptability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".