Identification of Faulty Sensor Nodes in WBAN Using Genetically Linked Artificial Neural Network
DOI:
https://doi.org/10.52866/ijcsm.2024.05.02.005Keywords:
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 .
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Copyright (c) 2024 israa albarazanchi, Haider Abdulshaheed, Mayasa M Abdulrahman , Jamal Fadhil Tawfeq
This work is licensed under a Creative Commons Attribution 4.0 International License.
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