Efficient data aggregation technique for medical wireless body sensor networks-Reference-Cited by-同舟云学术

Efficient data aggregation technique for medical wireless body sensor networks

Published:2022-02-19 Issue:5 Volume:89 Page:328-342
ISSN:2196-7113
Container-title:tm - Technisches Messen
language:en
Short-container-title:

Author:

Belhaj Mohamed Mbarka¹,Meddeb-Makhlouf Amel²,Fakhfakh Ahmed³,Kanoun Olfa⁴

Affiliation:

1. Laboratory of signals, systems, artificial intelligence and networks (SM@RTS), Digital Research Center of Sfax (CRNS) , University of Sfax , National School of Engineers of Gabes (ENIG) , Gabes , Tunisia

2. New Technologies and Telecom Systems Research Unit (NTS’COM) , Engineering School of Electronics and Telecommunications of Sfax (ENET’com) , Sfax , Tunisia

3. Laboratory of signals, systems, artificial intelligence and networks (SM@RTS), Digital Research Center of Sfax (CRNS) , University of Sfax , Engineering School of Electronics and Telecommunications of Sfax (ENET’com) , Sfax , Tunisia

4. Measurement and Sensor Technology (MST) , Chemnitz University of Technology (TU) , Chemnitz , Germany

Abstract

Abstract A central issue in Wireless Body Sensor Networks (WBSNs) is the large amount of measurement data for monitoring vital parameters, which need to be continuously measured, immediately processed and timely transmitted. This requires a big storage space and computing effort leading to a high-power consumption. Reducing the amount of transmitted data contributes significantly to an extension of the sensor operation time. In this contribution, we focus exactly at this aspect. We propose a data aggregation method based on Artificial Neural Networks (ANN) combining multiple physiological signals, which are the ElectroCardioGram (ECG), ElectroMyoGram (EMG) and Blood Pressure (BP), in one signal before transmission. The simulation and implementation results reveal a reduction of energy consumption to 87.32 %, ensuring a high accuracy level (80.53 %) and a relatively execution time (48.47 ms).

Publisher

Walter de Gruyter GmbH

Subject

Electrical and Electronic Engineering,Instrumentation

Link

https://www.degruyter.com/document/doi/10.1515/teme-2021-0075/pdf

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