Advertisement

Nodes Deployment Optimization Algorithm Based on Energy Consumption of Underwater Wireless Sensor Networks

  • Min Cui
  • Fengtong Mei
  • Qiangyi LiEmail author
  • Qiangnan Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11323)

Abstract

Underwater wireless sensor networks nodes deployment optimization problem is studied and underwater wireless sensor nodes deployment determines its capability and lifetime. If no underwater wireless sensor node is available in the monitoring area of underwater wireless sensor networks due to used up energy or any other reasons, the monitoring area where is not detected by any underwater wireless sensor node forms coverage holes. In order to improve the coverage of the underwater wireless sensor networks and prolong the lifetime of the underwater wireless sensor networks, based on the perception model, establish nodes detection model, combining with the data fusion. Because the underwater wireless sensor networks nodes coverage holes appear when the initial randomly deployment, a nodes deployment algorithm based on perception model of underwater wireless sensor networks is designed in this article. The simulation results show that this algorithm can effectively reduce the number of deployment underwater wireless sensor networks nodes, improve the efficiency of underwater wireless sensor networks coverage, reduce the underwater wireless sensor networks nodes energy consumption, prolong the lifetime of the underwater wireless sensor networks.

Keywords

Nodes deployment Optimization algorithm Underwater wireless sensor networks 

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.
    Li, S.W., Ma, D.Q., Li, Q.Y., Zhang, J.W., Zhang, X.: 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

  • Min Cui
    • 1
  • Fengtong Mei
    • 1
  • Qiangyi Li
    • 2
    Email author
  • Qiangnan Li
    • 2
  1. 1.Zhengzhou University of Industrial TechnologyHenan ZhengzhouChina
  2. 2.Henan University of Science and TechnologyHenan LuoyangChina

Personalised recommendations