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

israa albarazanchi; Haider Abdulshaheed; Mayasa M Abdulrahman; Jamal Fadhil Tawfeq

doi:10.52866/ijcsm.2024.05.02.005

Authors

israa albarazanchi utem https://orcid.org/0000-0002-6798-6295
Haider Abdulshaheed Computer Engineering Techniques Department, Baghdad College of Economic Sciences University, Baghdad – Iraq https://orcid.org/0000-0003-3963-875X
Mayasa M Abdulrahman Computer Engineering Department, College of Engineering, University of Baghdad, Baghdad, Iraq https://orcid.org/0000-0003-1002-6485
Jamal Fadhil Tawfeq Department of Medical Instrumentation Technical Engineering, Medical Technical College, Al-Farahidi University, Baghdad, Iraq https://orcid.org/0000-0002-3816-3345

DOI:

https://doi.org/10.52866/ijcsm.2024.05.02.005

Keywords:

WBAN, Faulty Sensor Nodes ,Genetically Linked Artificial Neural Network (GL-ANN)

Abstract

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 .