Belief Function Based Decision Fusion for Decentralized Target Classification in Wireless Sensor Networks

Sensors

Wenyu Zhang²  Zhenjiang Zhang¹ 

[1] School of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China; E-Mail

关键词: decision fusion;    distributed classification fusion;    belief function;    evidence theory;    wireless sensor networks;   

DOI  :  10.3390/s150820524

来源: mdpi

【 摘 要 】

Decision fusion in sensor networks enables sensors to improve classification accuracy while reducing the energy consumption and bandwidth demand for data transmission. In this paper, we focus on the decentralized multi-class classification fusion problem in wireless sensor networks (WSNs) and a new simple but effective decision fusion rule based on belief function theory is proposed. Unlike existing belief function based decision fusion schemes, the proposed approach is compatible with any type of classifier because the basic belief assignments (BBAs) of each sensor are constructed on the basis of the classifier’s training output confusion matrix and real-time observations. We also derive explicit global BBA in the fusion center under Dempster’s combinational rule, making the decision making operation in the fusion center greatly simplified. Also, sending the whole BBA structure to the fusion center is avoided. Experimental results demonstrate that the proposed fusion rule has better performance in fusion accuracy compared with the naïve Bayes rule and weighted majority voting rule.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.