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

Abstract

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