Advertisement

Estimating Gas Source Location Based on Distributed Adaptive Deflection Projected Subgradient Method

  • Zhemin ZhuangEmail author
  • Fenlan Li
  • Ye Yuan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 699)

Abstract

A novel method based on distributed adaptive deflection projected subgradient is proposed in this paper. By used of attenuation model of a gas source in wind field, the method is able to process the distributed information from sensors by using the developed algorithm, replace the original gradient direction with deflection subgradient direction, and utilize the deflected subgradient projection hyperplanes as the searching areas in the process of relaxed projection, so as to obtain the gas source position. A related simulation provided in the paper illustrates that this method can not only provide good convergence property and accurate localization results, but also save large amount of energy.

Keywords

Wireless Sensor Network Gas source localization Distributed Deflection Projected subgradient 

Notes

Acknowledgements

This work was financially supported by the Foundation of China (No. 61471228) and the Key Project of Guangdong Province Science & Technology Plan (No. 2015B020233018).

References

  1. 1.
    Wang, C.L., Wu, D.S.: Decentralized positioning and tracking based on a weighted incremental subgradient algorithm for wireless sensor networks. In: Proceedings of IEEE Vehicular Technology Conference, Canada, pp. 1–5 (2008)Google Scholar
  2. 2.
    Sundhar, R.S., Nedic, A., Veeravalli, V.: Distributed subgradient projection algorithm for convex optimization. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Taiwan, pp. 3653–3656 (2009)Google Scholar
  3. 3.
    Blatt, D., Hero, A.O.: Energy-based sensor network source localization via projection onto convex sets. IEEE Trans. Sig. Process. 54, 3614–3619 (2006)CrossRefGoogle Scholar
  4. 4.
    Fukazawa, Y., Ishida, H.: Estimating gas-source location in outdoor environment using mobile robot equipped with gas sensors and anemometer. In: Proceedings of IEEE Sensors Conference, New Zealand, pp. 1721–1724 (2009)Google Scholar
  5. 5.
    Crank, J.: The Mathematics of Diffusion. Oxford Uni. Press, Oxford (1956)zbMATHGoogle Scholar
  6. 6.
    Zhang, Y., Wang, L.: A particle filtering method for odor-source localization in wireless sensor network with mobile robot. In: Proceedings of 8th World Congress on International Control and Automation, China, pp. 7032–7036 (2010)Google Scholar
  7. 7.
    Ampeliotis, D., Berberidis, K.: Low complexity multiple acoustic source localization in sensor networks based on energy measurements. Sig. Process. 90, 1300–1312 (2010)CrossRefzbMATHGoogle Scholar
  8. 8.
    Yukawa, M., Slavakis, K.: Signal processing dual domain by adaptive projection subgradient method. In: Proceedings of International Conference Digital Signal Processing, Greece (2009)Google Scholar
  9. 9.
    Nedic, A., Ozdaqlar, A., Parrilo, P.: Constrained consensus and optimization in multi-agent networks. IEEE Trans. Autom. Control 55, 922–938 (2010)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Yamadaa, I., Ogura, N.: Adaptive projected subgradient method for asymptotic minimization of sequence of nonnegative convex functions. Numer. Funct. Anal. Optim. 25, 593–617 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Stark, H., Yang, Y.: Vector Space Projections—A Numerical Approach to Signal and Image Processing, Neural Nets, and Optics. Wiley, New York (1998)zbMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Electronics EngineeringShantou UniversityGuangdongChina

Personalised recommendations