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
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
Jing, H.C.: Node deployment algorithm based on perception model of wireless sensor network. Int. J. Autom. Technol. 9(3), 210–215 (2015)
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)
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)
Jing, H.C.: Improved ultrasonic CT imaging algorithm of concrete structures based on simulated annealing. Sens. Transducers 162(1), 238–243 (2014)
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)
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)
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)
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)
Jing, H.C.: Improving SAFT imaging technology for ultrasonic detection of concrete structures. J. Appl. Sci. 13(21), 4363–4370 (2013)
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)
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)
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)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Song, X., Gong, Y., Jin, D., Li, Q., Jing, H. (2018). Nodes Deployment Optimization Algorithm Based on Improved Evidence Theory. In: Hu, T., Wang, F., Li, H., Wang, Q. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11338. Springer, Cham. https://doi.org/10.1007/978-3-030-05234-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-05234-8_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05233-1
Online ISBN: 978-3-030-05234-8
eBook Packages: Computer ScienceComputer Science (R0)