Identification of Faulty Sensor Nodes in WBAN Using Genetically Linked Artificial Neural Network

Wireless Body Area Networks (WBANs) have risen as a promising innovation for checking human physiological parameters in real time. Be that as it may, the unwavering quality and precision of WBANs depend on the right working of sensor hubs. The distinguishing proof of defective sensor hubs is vital for guaranteeing the quality of information collected by WBANs. In this paper, we propose a novel approach to recognizing faulty sensor hubs in WBAN employing a hereditarily linked artificial neural organize (GLANN). The GLANN is prepared to employ a crossbreed fuzzy-genetic calculation to optimize its execution in distinguishing defective sensor hubs. The proposed approach is assessed employing a dataset collected from a real-world WBAN. The comes about appears that the GLANN-based approach beats existing strategies in terms of exactness and proficiency. The proposed approach has potential applications within the field of healthcare, where exact and solid observing of human physiological parameters is basic for conclusion and treatment. By and large, this ponder presents a promising approach to progressing the unwavering quality and exactness of WBANs by identifying flawed sensor hubs utilizing GLANN .