Abstract
Decision fusion is an important issue in wireless sensor networks (WSN), and intuitionistic fuzzy set (IFS) is a novel method for dealing with uncertain data. We propose a multi-attribute decision fusion model based on IFS, which includes two aspects: data distribution-based IFS construction algorithm (DDBIFCA) and the category similarity weight-based TOPSIS intuitionistic fuzzy decision algorithm (CSWBT-IFS). The DDBIFCA is an IFS construction algorithm that transforms the original attribute values into intuitionistic fuzzy measures, and the CSWBT-IFS is an intuitionistic fuzzy aggregation algorithm improved by the traditional TOPSIS algorithm, which combines intuitionistic fuzzy values of different attributes and obtains a final decision for the monitoring target. Both algorithms have benefits, such as low energy consumption and low computational complexity, which make them suitable for implementation in energy-constrained WSNs. Simulation results show the efficiency of intuitionistic fuzzification for the DDBIFCA and a high classification accuracy, compared with traditional fuzzy fusion and other intuitionistic fuzzy aggregation algorithms, for the CSWBT-IFS.
Document Type
Article
Publication Date
7-3-2017
Digital Object Identifier (DOI)
https://doi.org/10.1109/ACCESS.2017.2722483
Funding Information
This work was supported by the National Natural Science Foundation of China under Grant 61271308 and Grant 61401015.
Repository Citation
Zhang, Zhenjiang; Hao, Ziqi; Zeadally, Sherali; Zhang, Jing; Han, Bowen; and Chao, Han-Chieh, "Multiple Attributes Decision Fusion for Wireless Sensor Networks Based on Intuitionistic Fuzzy Set" (2017). Information Science Faculty Publications. 49.
https://uknowledge.uky.edu/slis_facpub/49
Notes/Citation Information
Published in IEEE Access, v. 5, p. 12798-12809.
© 2017 IEEE
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