Nodes Deployment Optimization Algorithm Based on Improved Evidence Theory
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.
KeywordsEvidence 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 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.
- 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.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
- 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
- 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.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.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
- 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
- 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.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.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