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


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.


Chemical source localization Sensor networks Gaussian mixture particle filters 



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.


  1. 1.
    Patwari, N., Ash, J.N., Kyperountas, S., et al.: Locating the nodes: cooperative localization in wireless sensor networks. IEEE Signal Process. Mag. 22(4), 54–69 (2005)CrossRefGoogle Scholar
  2. 2.
    Han, G., Xu, H., Duong, T.Q.: Localization algorithms of wireless sensor networks: a survey. Telecommun. Syst. 52(4), 2419–2436 (2013)CrossRefGoogle Scholar
  3. 3.
    Xu, E., Ding, Z., Dasgupta, S.: Source localization in wireless sensor networks from signal time-of-arrival measurements. IEEE Trans. Signal Process. 59(6), 2887–2897 (2011)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Shu, L., Mukherjee, M., Xu, X.: A survey on gas leakage source detection and boundary tracking with wireless sensor networks. IEEE Access 4, 1700–1715 (2016)CrossRefGoogle Scholar
  5. 5.
    Hutchinson, M., Oh, H., Chen, W.H.: A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors. Inf. Fusion 36(7), 130–148 (2017)CrossRefGoogle Scholar
  6. 6.
    Chraim, F., Erol, Y.B., Pister, K.: Wireless gas leak detection and localization. IEEE Trans. Ind. Inform. 12(2), 768–779 (2016)CrossRefGoogle Scholar
  7. 7.
    Nehorai, A., Porat, B., Paidi, E.: Detection and localization of vapor-emitting sources. IEEE Trans. Signal Process. 43(1), 243–253 (1995)CrossRefGoogle Scholar
  8. 8.
    Jeremic, A., Nehorai, A.: Landmine detection and localization using chemical sensor array processing. IEEE Trans. Signal Process. 48(5), 1295–1305 (2000)CrossRefGoogle Scholar
  9. 9.
    Vijayakumaran, S., Levinbook, Y., Wong, T.F.: Maximum likelihood localization of a diffusive point source using binary observations. IEEE Trans. Signal Process. 55(2), 665–676 (2007)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Matthes, J., Groll, L., Keller, H.R.: Source localization by spatially distributed electronic noses for advection and diffusion. IEEE Trans. Signal Process. 53(5), 1711–1719 (2005)CrossRefzbMATHMathSciNetGoogle Scholar
  11. 11.
    Michaelides, M.P., Panayiotou, C.G.: Plume source position estimation using sensor networks. In: Proceedings of the 2005 IEEE International Symposium on Mediterranean Conference on Control and Automation, 2005, pp. 731–736Google Scholar
  12. 12.
    Kuang, X.H., Shao, H.H.: Study of the two plume source localization algorithms based on WSN. Chin. J. Sci. Instrum. 28(2), 298–302 (2007)Google Scholar
  13. 13.
    Zhao, F., Shin, J., Reich, J.: Information-driven dynamic sensor collaboration. IEEE Signal Process. Mag. 19(2), 61–72 (2002)CrossRefGoogle Scholar
  14. 14.
    Keats, A.W., Yee, E., Lien, F.S.: Bayesian inference for source determination with applications to a complex urban environment. Atmos. Environ. 41, 465–479 (2007)CrossRefGoogle Scholar
  15. 15.
    Keats, W.A.: Bayesian inference for source determination in the atmospheric environment. Doctoral Dissertation, University of Waterloo, 2009Google Scholar
  16. 16.
    Zhao, T., Nehorai, A.: Distributed sequential Bayesian estimation of a diffusive source in wireless sensor networks. IEEE Trans. Signal Process. 55(4), 1511–1524 (2007)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Zhao, T., Nehorai, A.: Information-driven distributed maximum likelihood estimation based on Gauss–Newton method in wireless sensor networks. IEEE Trans. Signal Process. 55(9), 4669–4682 (2007)CrossRefMathSciNetGoogle Scholar
  18. 18.
    Li, J.G., Meng, Q.H., Wang, Y., et al.: Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm. Auton. Robots 30(3), 281–292 (2011)CrossRefGoogle Scholar
  19. 19.
    Ristic, B., Gunatilaka, A., Gailis, R.: Achievable accuracy in Gaussian plume parameter estimation using a network of binary sensors. Inf. Fusion 25, 42–48 (2015)CrossRefGoogle Scholar
  20. 20.
    Zhang, Y., Meng, Q.H., Wu, Y.X., Zeng, M.: Gas leakage source localization algorithm based on distributed MMSE sequential estimation. Chin. J. Sens. Actuators 27(1), 128–134 (2014)Google Scholar
  21. 21.
    Zhang, Y., Meng, Q.H., Wu, Y.X., Zeng, M.: Parameter determination of biochemical odor source using distributed algorithm in sensors network. J. Tianjin Univ. 05, 448–453 (2012)MathSciNetGoogle Scholar
  22. 22.
    Yu, J.: A particle filter driven dynamic Gaussian mixture model approach for complex process monitoring and fault diagnosis. J. Process Control 22(4), 778–788 (2012)CrossRefGoogle Scholar
  23. 23.
    Yn, F., Fritsche, C., Jin, D.: Cooperative localization in WSNs using Gaussian mixture modeling: distributed ECM algorithms. IEEE Trans. Signal Process. 63(6), 1448–1463 (2015)CrossRefGoogle Scholar
  24. 24.
    Mohammadi, A., Asif, A.: Decentralized conditional posterior Cramér–Rao lower bound for nonlinear distributed estimation. IEEE Signal Process. Lett 20(2), 165–168 (2013)CrossRefGoogle Scholar
  25. 25.
    Fangfang, P., Shuli, S.: Distributed fusion estimation for multisensor multirate systems with stochastic observation multiplicative noises. Math. Probl. Eng. 8, 1–8 (2014)CrossRefMathSciNetGoogle Scholar
  26. 26.
    Kaplan, L.M.: Local node selection for localization in a distributed sensor network. IEEE Trans. Aerosp. Electron. Syst. 42(1), 136–146 (2006)CrossRefGoogle Scholar

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

Personalised recommendations