ANOMALY DIAGNOSIS IN WIRELESS BODY AREA NETWORKS
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Date
2024-06
Journal Title
Journal ISSN
Volume Title
Publisher
UNIVERSITY OF MOHAMED BOUDIAF – MSILA, FACULTY OF MATHEMATICS AND COMPUTER SCIENCE, DEPARTMENT OF COMPUTER SCIENCE
Abstract
The primary focus of this work is on fault detection in Wireless Body Area
Networks (WBANs). It emphasizes the critical importance of ensuring that
information and signals from WBAN devices are accurately and reliably
transmitted, particularly in the context of health monitoring.
Additionally, this work aims to address the challenges related to fault
detection in WBANs by conducting a comparative analysis of various
machine learning algorithms (ML) and statistics.
Description
Keywords
Wireless Body Area Networks, fault detection, machine learning, algorithms, statistics