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.
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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.
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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
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DOI: https://doi.org/10.1007/978-3-030-05234-8_11
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