ANOMALY DIAGNOSIS IN WIRELESS BODY AREA NETWORKS

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.