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Sensors 2014, 14(4), 7049-7065; doi:10.3390/s140407049

Novel Paradigm for Constructing Masses in Dempster-Shafer Evidence Theory for Wireless Sensor Network’s Multisource Data Fusion

Department of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China
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Received: 23 January 2014 / Revised: 12 April 2014 / Accepted: 15 April 2014 / Published: 22 April 2014
(This article belongs to the Section Sensor Networks)
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Abstract

Dempster-Shafer evidence theory (DSET) is a flexible and popular paradigm for multisource data fusion in wireless sensor networks (WSNs). This paper presents a novel and easy implementing method computing masses from the hundreds of pieces of data collected by a WSN. The transfer model is based on the Mahalanobis distance (MD), which is an effective method to measure the similarity between an object and a sample. Compared to the existing methods, the proposed method concerns the statistical features of the observed data and it is good at transferring multi-dimensional data to belief assignment correctly and effectively. The main processes of the proposed method, which include the calculation of the intersection classes of the power set and the algorithm mapping MDs to masses, are described in detail. Experimental results in transformer fault diagnosis show that the proposed method has a high accuracy in constructing masses from multidimensional data for DSET. Additionally, the results also prove that higher dimensional data brings higher accuracy in transferring data to mass. View Full-Text
Keywords: mass; Dempster-Shafer evidence theory; Mahalanobis Distance; WSN mass; Dempster-Shafer evidence theory; Mahalanobis Distance; WSN
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Zhang, Z.; Liu, T.; Zhang, W. Novel Paradigm for Constructing Masses in Dempster-Shafer Evidence Theory for Wireless Sensor Network’s Multisource Data Fusion. Sensors 2014, 14, 7049-7065.

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