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

Wenyu Zhang1, Zhenjiang Zhang2

  • 1School of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China. zhangwenyu@bjtu.edu.cn.

Sensors (Basel, Switzerland)
|August 22, 2015
PubMed
Summary
This summary is machine-generated.

A new belief function-based decision fusion rule for wireless sensor networks (WSNs) improves classification accuracy. This method is compatible with any classifier and simplifies fusion center operations, outperforming existing rules.

Keywords:
belief functiondecision fusiondistributed classification fusionevidence theorywireless sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Decision fusion in sensor networks enhances classification accuracy and reduces data transmission demands.
  • Wireless sensor networks (WSNs) face challenges in decentralized multi-class classification due to energy and bandwidth constraints.

Purpose of the Study:

  • To propose a novel, simple, and effective decision fusion rule for decentralized multi-class classification in WSNs.
  • To develop a fusion rule compatible with any classifier by utilizing confusion matrices and real-time observations.

Main Methods:

  • A new decision fusion rule based on belief function theory is introduced.
  • Basic belief assignments (BBAs) are constructed using classifier confusion matrices and real-time sensor data.
  • Dempster's combinational rule is used to derive an explicit global BBA at the fusion center, simplifying decision-making.

Main Results:

  • The proposed fusion rule demonstrates superior performance in fusion accuracy compared to Naïve Bayes and weighted majority voting rules.
  • The method avoids transmitting the entire BBA structure to the fusion center, reducing communication overhead.
  • The approach is compatible with diverse classifiers, enhancing its applicability.

Conclusions:

  • The proposed belief function-based decision fusion rule offers an effective solution for decentralized multi-class classification in WSNs.
  • This method enhances classification accuracy while simplifying fusion center operations and reducing communication costs.
  • The compatibility with various classifiers makes it a versatile approach for sensor network applications.