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Research on Reconstruction Method of Random Missing Sensor Data Based on Fuzzy Logic Theory

  • Liang ZhaoEmail author
  • Kai Liu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)

Abstract

Wireless sensor nodes are often deployed in the wild environment, and the data collected are often lost. It is very important to reconstruct the missing data for accurate scientific calculation or other applications. In this study, a random missing data reconstruction method based on fuzzy logic theory is presented. The method mainly studies how to combine the Euclidean distance between the sensor nodes and the correlation of the sensory data to construct a new method of determining neighbor nodes, while the weight calculation method of each neighbor node participating in reconstruction is studied, which is to solve the deficiencies of the neighbor node selection when there are obstacles between sensor nodes only rely on the Euclidean distance. The experimental results show that the accuracy of the proposed method is relatively high when the sensor data has a mutation or the acquisition time interval is large.

Keywords

Wireless sensor networks Missing data reconstruction Fuzzy logic theory Spatial correlation 

Notes

Acknowledgement

This study is supported by the Natural Science Foundation of Hubei Province of China (Program No. 2016CKB705).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.College of InformaticsHuazhong Agricultural UniversityWuhanChina

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