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Cluster Computing

, Volume 20, Issue 4, pp 2905–2917 | Cite as

Distributed Gaussian mixture model-based particle filter method for chemical pollution source localization with sensor network

  • Yong ZhangEmail author
  • Liyi Zhang
  • Jianfeng Han
  • Zhe Ban
Article

Abstract

Chemical pollution source localization with statistical estimation algorithm in sensor networks, which was also known as source parameters estimation, has an important significance in fields such as pollution environmental monitoring and control. In this paper, a distributed Gaussian mixture dispersion model based particle filter method was proposed for the chemical pollution source localization problem. At the same time, we designed a composite information objective function for sensor scheduling scheme, which comprised of information utility measurement and energy consumption measurement. At last, in order to balance the source localization accuracy and energy consumption, a dynamical sensor radius adjusting method was given for sensor nodes scheduling. Simulation and experiment results show that the proposed method could determine the position of chemical pollution source, compared to UKF, the distributed Gaussian mixture particle filter method was suggested because it could get a significant reduction in the required numbers of sensor nodes and less energy to achieve the desired performance with less time.

Keywords

Chemical source localization Sensor networks Gaussian mixture particle filters 

Notes

Acknowledgements

The authors wish to thank for the financial support of Natural Science Foundation of China (61573253, 61271321), Tianjin Natural Science Foundation (16JCYBJC16400), Tianjin Science and Technology Project (16YFZCGX00360, 16ZXZNGX00080), Tianjin Higher Educational Science and Technology Development Fund Project (20130710), National Training Programs of Innovation and Entrepreneurship for Undergraduates (201610069007). Natural Science Nurturing Fund of Tianjin University of Commerce. The corresponding author is Professor Zhang Liyi.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Yong Zhang
    • 1
    • 2
    Email author
  • Liyi Zhang
    • 1
    • 2
  • Jianfeng Han
    • 1
  • Zhe Ban
    • 1
  1. 1.The College of InformationTianjin University of CommerceTianjinChina
  2. 2.School of Electronic Information EngineeringTianjin UniversityTianjinChina

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