Photonic Network Communications

, Volume 37, Issue 2, pp 224–232 | Cite as

Nodes deployment optimization algorithm based on improved evidence theory of underwater wireless sensor networks

  • Xiaoli SongEmail author
  • Yunzhan Gong
  • Dahai Jin
  • Qiangyi Li
Original Paper


Underwater wireless sensor networks (UWSNs) applications for ocean monitoring, deep sea surveillance, and locating natural resources are gaining more and more popularity. To monitor the underwater environment or any object within a certain area of interest, these applications are required to deploy underwater node sensors connected for obtaining useful data. For thriving UWSNs, it is essential that an efficient and secure node deployment mechanism is in place. This paper presents a novel node deployment scheme, which is based on evidence theory approach and caters for 3D USWNs. 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 our algorithm deploys fewer nodes with enhanced network judgment criteria and expanded detection capabilities for a relatively large coverage area compared to other schemes. In addition, the generated nodes are also less resource intensive, i.e., low-power sensor nodes.


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



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


  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, 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.
    Song, Ruizhuo, Wei, Qinglai, Xiao, Wendong: ADP-based optimal sensor scheduling for target tracking in energy harvesting wireless sensor networks. Neural Comput. Appl. 27(6), 1543–1551 (2016)CrossRefGoogle Scholar
  7. 7.
    Hwang, Soyoung, Donghui, Yu.: Data forwarding based on sensor device constraints in wireless multimedia sensor networks. Multimed. Tools Appl. 68(2), 297–303 (2014)CrossRefGoogle 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.
    Li, Yantao, Zhou, Gang, Zheng, Nan, Hong, Liang: An adaptive backoff algorithm for multi-channel CSMA in wireless sensor networks. Neural Comput. Appl. 25(7–8), 1845–1851 (2014)CrossRefGoogle Scholar
  10. 10.
    Wu, N.N., Zhang, J.W., Li, Q.Y., Li, S.W., Liu, Y.C., Wang, Y.L., Fu, Z.W.: 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, 374–378 (2013)Google Scholar
  14. 14.
    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
  15. 15.
    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
  16. 16.
    Kelly, S.D.T., Suryadevara, N.K., Mukhopadhyay, S.C.: Towards the implementation of IoT for environmental condition monitoring in homes. IEEE Sens. J. 13(10), 3846–3853 (2013)CrossRefGoogle Scholar
  17. 17.
    Suryadevara, N.K., Mukhopadhyay, S.C., Wang, R., Rayudu, R.K.: Forecasting the behavior of an elderly using wireless sensors data in a smart home. Eng. Appl. Artif. Intell. 26(10), 2641–2652 (2013)CrossRefGoogle Scholar
  18. 18.
    Yan, H.L., Ji, C.C., Chen, G.L., Zhao, S.G.: Coverage and deployment analysis of 3D sensor nodes in wireless multimedia sensor networks. J. Comput. Inf. Syst. 8(15), 6159–6166 (2012)Google Scholar
  19. 19.
    Li, X., He, Y.Y.: A solution to the optimal density of heterogeneous surveillance sensor network in pin-packing coverage condition. J. Comput. Inf. Syst. 8(17), 7029–7036 (2012)Google Scholar
  20. 20.
    Suryadevara, N.K., Mukhopadhyay, S.C.: Wireless sensor network based home monitoring system for wellness determination of elderly. IEEE Sens. J. 12(6), 1965–1972 (2012)CrossRefGoogle Scholar
  21. 21.
    Wei, L.N., Qin, Z.G.: On-line bi-objective coverage hole healing in hybrid wireless sensor networks. J. Comput. Inf. Syst. 8(13), 5649–5658 (2012)Google Scholar
  22. 22.
    Unaldi, N., Temel, S., Asari, V.K.: Method for optimal sensor deployment on 3D terrains utilizing a steady state genetic algorithm with a guided walk mutation operator based on the wavelet transform. Sensors 12(4), 5116–5133 (2012)CrossRefGoogle Scholar
  23. 23.
    Zhao, X.M., Mao, K.J., Yang, F., Wang, W.F., Chen, Q.Z.: Research on detecting sensing coverage hole algorithm based on OGDC for wireless sensor networks. J. Comput. Inf. Syst. 8(20), 8561–8568 (2012)Google Scholar
  24. 24.
    Li, M., Shi, W.R.: Virtual force-directed differential evolution algorithm based coverage-enhancing algorithm for heterogeneous mobile sensor networks. Chin. J. Sci. Instrum. 32(5), 1043–1050 (2011)CrossRefGoogle Scholar
  25. 25.
    Chizari, H., Hosseini, M., Poston, T., Razak, S.A., Abdullah, A.H.: Delaunay triangulation as a new coverage measurement method in wireless sensor network. Sensors 11(3), 3163–3176 (2011)CrossRefGoogle Scholar
  26. 26.
    Zhang, R.B., Zhou, F., Ran, L., Shen, M.: A fuzzy graph theory based redundant node deployment algorithm for multi-hop WSN. Chin. High Technol. Lett. 21(3), 223–224 (2011)Google Scholar
  27. 27.
    Zhang, Z.J., Xin, Y.: An algorithm for guiding mobile nodes in wireless sensor networks based on a fuzzy logic controller. Chin. High Technol. Lett. 21(6), 562–568 (2011)Google Scholar
  28. 28.
    Ozturk, C., Karaboga, D., Gorkemli, B.: Probabilistic dynamic deployment of wireless sensor networks by artificial bee colony algorithm. Sensors 11(6), 6056–6065 (2011)CrossRefGoogle Scholar
  29. 29.
    Zhang, C.L., Bai, X.L., Teng, J., Xuan, D., Jia, W.J.: Constructing low-connectivity and full-coverage three dimensional sensor networks. IEEE J. Sel. Areas Commun. 28(7), 984–993 (2010)CrossRefGoogle Scholar
  30. 30.
    Li, M., Shi, W.R.: Optimal multi-objective sensor deployment scheme based on differential evolution algorithm in heterogeneous sensor networks. Chin. J. Sci. Instr. 31(8), 1896–1903 (2010)Google Scholar
  31. 31.
    Fan, G.J., Wang, R.C., Huang, H.P., Sun, L.J., Sha, C.: Coverage-guaranteed sensor node deployment strategies for wireless sensor networks. Sensors 10(3), 2064–2087 (2010)CrossRefGoogle Scholar
  32. 32.
    Ammari, H.M., Das, S.K.: A study of k-coverage and measures of connectivity in 3D wireless sensor networks. IEEE Trans. Comput. 59(2), 243–257 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Duan, H.Y.: Research on collaboration in innovative methods of manufacturing innovation chain. Revista Iberica de Sistemas e Tecnologias de Informacao E11, 292–303 (2016)Google Scholar
  34. 34.
    Chen, A., Kumar, S., Lai, T.H.: Local barrier coverage in wireless sensor networks. IEEE Trans. Mob. Comput. 9(4), 491–504 (2010)CrossRefGoogle Scholar
  35. 35.
    Zhang, H.S., Zhou, Z.N., Pan, C., Yang, J., Jia, L.M.: Particle swarm optimization approach of wireless sensor network node deployment for traffic information acquisition. Chin. J. Sci. Instr. 31(9), 1991–1996 (2010)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

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

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