Node Position Estimation and Coverage Hole Detection in Wireless Sensor Networks Using Clustering-Guided Twin Contrastive Learning with Meerkat Optimization Algorithm

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.