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RELIABILITY ANALYSIS FOR SENSOR NETWORKS AND THEIR DATA ACQUISITION: A SYSTEMATIC LITERATURE REVIEW

Published online by Cambridge University Press:  19 June 2023

Sören Meyer zu Westerhausen ,
Jannik Schneider  and
Roland Lachmayer
Sören Meyer zu Westerhausen*
Affiliation:
Institut für Produktentwicklung und Gerätebau
Jannik Schneider
Affiliation:
Institut für Produktentwicklung und Gerätebau
Roland Lachmayer
Affiliation:
Institut für Produktentwicklung und Gerätebau
*
Meyer zu Westerhausen, Sören, Institut of Product Developement, Germany, meyer-zu-westerhausen@ipeg.uni-hannover.de

Abstract

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The use of sensor networks (SNs) on the surface or the inside of large-scale components allows the continuous acquisition of data on the applied loads and their structural integrity. A lot of publications on SN's system reliability deal with this topic from a hardware- or a data- and energy-oriented viewpoint. To give an overview on the state of the art in the field of reliability-oriented concept-optimization of SNs, a Systematic Literature Review is conducted. The found literature is used to investigate how different models combine the different viewpoints to analyse the system reliability. By analysing the results regarding the used reliability indicators and methods to assess the system reliability from the different viewpoints, it can be observed that most publications deal with the accuracy, loss and delay of data as well as the energy consumption in SNs. Few publications use common modelling methods like reliability block diagrams or Markov chains with a focus on the hardware reliability. Furthermore, none of the found publications combines the data, hardware and energy perspective and uses them to optimize a SN regarding its reliability from all three viewpoints.

Keywords

Product modelling / modelsOptimisationDesign methodsReliabilitySensor Networks
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 3065 - 3074
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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