Advertisement

Nodes Deployment Optimization Algorithm Based on Improved Evidence Theory

  • Xiaoli Song
  • Yunzhan Gong
  • Dahai Jin
  • Qiangyi LiEmail author
  • Hengchang Jing
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11338)

Abstract

Underwater wireless sensor networks (UWSNs) applications for ocean monitoring, deep sea surveillance, and locating natural resources are gaining popularity. To monitor the underwater environment or any object of interest, these applications are required to deploy underwater connected node sensors for obtaining useful data. For thriving UWSNs, it is essential that an efficient and secure node deployment mechanism is in place. In this article, we are presenting a novel nodes deployment scheme which is based on evidence theory approach and cater-for 3D-UWSNs. This scheme implements sonar probability perception and an enhanced data fusion model to improve prior probability deployment algorithm of D-S evidence theory. The viability of our algorithm is verified by performing multiple simulation experiments. The simulation results reveal that as compared to other schemes, our algorithm deploys fewer nodes with enhanced network judgment criteria and expanded detection capabilities for a relatively large area.

Keywords

Evidence theory Nodes deployment algorithm Underwater wireless sensor networks Data fusion Coverage 

Notes

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (NSFC) under Grant No. U1736110 and the Soft Scientific Research Projects in Henan Province, China under Grant No. 172400410013. The authors also gratefully acknowledge the helpful comments and suggestions of the editors and reviewers, which have improved the presentation.

References

  1. 1.
    Song, X.L., Gong, Y.Z., Jin, D.H., Li, Q.Y., Jing, H.C.: Coverage hole recovery algorithm based on molecule model in heterogeneous WSNs. Int. J. Comput. Commun. Control 12(4), 562–576 (2017)CrossRefGoogle Scholar
  2. 2.
    Song, X.L., Gong, Y.Z., Jin, D.H., Li, Q.Y., Zheng, R.J., Zhang, M.C.: Nodes deployment based on directed perception model of wireless sensor networks. J. Beijing Univ. Posts Telecommun. 40, 39–42 (2017)Google Scholar
  3. 3.
    Zhao, M.Z., Liu, N.Z., Li, Q.Y.: Blurred video detection algorithm based on support vector machine of schistosoma japonicum miracidium. In: International Conference on Advanced Mechatronic Systems, pp. 322–327 (2016)Google Scholar
  4. 4.
    Jing, H.C.: Node deployment algorithm based on perception model of wireless sensor network. Int. J. Autom. Technol. 9(3), 210–215 (2015)CrossRefGoogle Scholar
  5. 5.
    Jing, H.C.: Routing optimization algorithm based on nodes density and energy consumption of wireless sensor network. J. Comput. Inf. Syst. 11(14), 5047–5054 (2015)Google Scholar
  6. 6.
    Jing, H.C.: The study on the impact of data storage from accounting information processing procedure. Int. J. Database Theory Appl. 8(3), 323–332 (2015)CrossRefGoogle Scholar
  7. 7.
    Jing, H.C.: Improved ultrasonic CT imaging algorithm of concrete structures based on simulated annealing. Sens. Transducers 162(1), 238–243 (2014)Google Scholar
  8. 8.
    Zhang, J.W., Li, S.W., Li, Q.Y., Liu, Y.C., Wu, N.N.: Coverage hole recovery algorithm based on perceived probability in heterogeneous wireless sensor network. J. Comput. Inf. Syst. 10(7), 2983–2990 (2014)Google Scholar
  9. 9.
    Jing, H.C.: Coverage holes recovery algorithm based on nodes balance distance of underwater wireless sensor network. Int. J. Smart Sens. Intell. Syst. 7(4), 1890–1907 (2014)Google Scholar
  10. 10.
    Wu, N.N., et al.: Mobile nodes deployment scheme design based on perceived probability model in heterogeneous wireless sensor network. J. Robot. Mechatron. 26(5), 616–621 (2014)CrossRefGoogle Scholar
  11. 11.
    Li, Q.Y., Ma, D.Q., Zhang, J.W.: Nodes deployment algorithm based on perceived probability of wireless sensor network. Comput. Meas. Control. 22(2), 643–645 (2014)Google Scholar
  12. 12.
    Jing, H.C.: Improving SAFT imaging technology for ultrasonic detection of concrete structures. J. Appl. Sci. 13(21), 4363–4370 (2013)CrossRefGoogle Scholar
  13. 13.
    Shi-Wei, L., Dong-Qian, M., Qiang-Yi, L., Ju-Wei, Z., Xue, Z.: Nodes deployment algorithm based on perceived probability of heterogeneous wireless sensor network. In: International Conference on Advanced Mechatronic Systems, pp. 374–378 (2013)Google Scholar
  14. 14.
    Zhang, H.T., Bai, G., Liu, C.P.: Improved simulated annealing algorithm for broadcast routing of wireless sensor network. J. Comput. Inf. Syst. 9(6), 2303–2310 (2013)Google Scholar
  15. 15.
    Li, Q.Y., Ma, D.Q., Zhang, J.W., Fu, F.Z.: Nodes deployment algorithm of wireless sensor network based on evidence theory. Comput. Meas. Control. 21(6), 1715–1717 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xiaoli Song
    • 1
    • 2
  • Yunzhan Gong
    • 1
  • Dahai Jin
    • 1
  • Qiangyi Li
    • 2
    Email author
  • Hengchang Jing
    • 2
  1. 1.State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Henan University of Science and TechnologyHenanChina

Personalised recommendations