An Advanced Weighted Evidence Combination Method for Multisensor Data Fusion in IoT

Dempster-Shafer theory is an essential tool for modelling and reasoning under uncertainty, and it is an effective approach for multisensor data fusion. It is extensively deployed in many fields such as fault diagnosis, image processing, pattern recognition, etc. However, Dempster's combination...

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Bibliographic Details
Published in2022 International Conference on Decision Aid Sciences and Applications (DASA) pp. 810 - 815
Main Authors Hamda, Nour El Imane, Hadjali, Allel, Lagha, Mohand
Format Conference Proceeding
LanguageEnglish
Published IEEE 23.03.2022
Subjects
Benchmark testing
Data integration
Data models
Dempster-Shafer theory
Deng entropy
Entropy
evidence angle
evidence distance
Fault diagnosis
Image processing
IoT
Multisensor data fusion
Uncertainty
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Summary:Dempster-Shafer theory is an essential tool for modelling and reasoning under uncertainty, and it is an effective approach for multisensor data fusion. It is extensively deployed in many fields such as fault diagnosis, image processing, pattern recognition, etc. However, Dempster's combination rule is often subject to counter-intuitive results when the sources highly conflict; several methods have been proposed in the literature to solve this problem. In this paper, a weighted evidence combination method based on evidence distance, evidence angle, and information volume is proposed to overcome the shortcomings of the classical Dempster's combination rule. To investigate the effectiveness and performance of the proposed method, a comparative study with different state of the art methods using both benchmark numerical example and Fault diagnosis application has been carried out.
DOI:10.1109/DASA54658.2022.9765125