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Vol. 157, Issue 10, October 2013, pp. 20-25




An Improved Fusion Method of Fuzzy Logic Based on k-mean Clustering in WSN
1, 2 Feng WANG, 2 Kang WANG, 2 Fuzhen JIANG

1 College of Information Engineering, Taiyuan University of Technology, Shanxi Taiyuan 030024, People's Republic of China

2 Faculty of Resource & Safety Engineering, China University of Mining & Technology Beijing 100083, People's Republic of China

1Tel.: 13033488721

1 E-mail: wangfeng@tyut.edu.cn


Received: 11 July 2013   /Accepted: 25 September 2013   /Published: 31 October 2013

Digital Sensors and Sensor Sysstems


Abstract: Data fusion in wireless sensor networks is one of the key Technologies. In this paper, for the problem of large deviation of data fusion based on weighted fuzzy logic algorithm in wireless sensor networks, a new method is proposed. First to eliminate the flawed data through analysis of initial data using the idea of k-mean clustering, and to revise the weighting factors of weighted fuzzy logic algorithm with the rest authentic data, and then integrate all data by means of weighted method to get the final fusion result. Experimental results show that this method can achieve higher integration accuracy compared with the other same fusion methods.


Keywords: Data fusion, Fuzzy logic algorithm, Wireless sensor networks, K-mean clustering, Weighting factors.


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