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Multiple Attributes Decision Fusion for Wireless Sensor Networks Based on Intuitionistic Fuzzy Set - 2017

Research Area:  Wireless Sensor Networks

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.

Author(s) Name:  Zhenjiang Zhang,Ziqi Hao,Sherali Zeadally,Jing Zhang,Bowen Han and Han-Chieh Chao

Journal name:  IEEE Access

Conferrence name:  

Publisher name:  IEEE

DOI:   10.1109/ACCESS.2017.2722483

Volume Information:  Volume: 5, Page(s): 12798 - 12809