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Efficient data aggregation technique for medical wireless body sensor networks

Authors
  • Belhaj Mohamed, Mbarka1
  • Meddeb-Makhlouf, Amel2
  • Fakhfakh, Ahmed3
  • Kanoun, Olfa4
  • 1 University of Sfax, National School of Engineers of Gabes (ENIG) , (Tunisia)
  • 2 Engineering School of Electronics and Telecommunications of Sfax (ENET’com), Tunisia , (Tunisia)
  • 3 University of Sfax, Engineering School of Electronics and Telecommunications of Sfax (ENET’com) , (Tunisia)
  • 4 Chemnitz University of Technology (TU), Germany , (Germany)
Type
Published Article
Journal
tm - Technisches Messen
Publisher
De Gruyter Oldenbourg
Publication Date
Feb 19, 2022
Volume
89
Issue
5
Pages
328–342
Identifiers
DOI: 10.1515/teme-2021-0075
Source
De Gruyter
Keywords
Disciplines
  • Beiträge
License
Yellow

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).

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