Open AccessToward fast and accurate emergency cases detection in BSNs
Author(s) -
Boudargham Nadine,
El Sibai Rayane,
Bou Abdo Jacques,
Demerjian Jacques,
Guyeux Christophe,
Makhoul Abdallah
Publication year - 2020
Publication title -
iet wireless sensor systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.433
H-Index - 27
ISSN - 2043-6394
DOI - 10.1049/iet-wss.2019.0134
Subject(s) - cusum , computer science , adaptive sampling , wireless sensor network , real time computing , sampling (signal processing) , default gateway , data mining , wireless , statistics , computer network , telecommunications , detector , mathematics , monte carlo method
In body sensor networks (BSNs), medical sensors capture physiological data from the human body and send them to the coordinator who act as a gateway to health care. The main aim of BSNs is to save peoples’ lives. Therefore, fast and correct detection of emergencies while maintaining low‐energy consumption of sensors is essential requirement of BSNs. In this study, the authors propose a new adaptive data sampling approach, where the sampling ratio is adapted based on the sensed data variation. The idea is to use the modified version of the cumulative sum (CUSUM) algorithm (modified CUSUM) that they previously proposed for wireless sensor networks to monitor the data variability, and adapt the sampling rate accordingly. Modified CUSUM is then applied to the adaptively sampled data to detect anomalies, and the correlation property between physiological parameters will be used to identify emergency cases from false alarms. Several experiments are performed and compared to evaluate the efficiency of their approach, and different parameters are considered.