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4 May 2019 Location and Energy Saving of Underwater Sensor Network for Monitoring Seabed Landslide
Yan Xu, Hongxian Shan, Yonggang Jia
Author Affiliations +
Abstract

Xu, Y.; Shan, H., and Jia, Y., 2018. Location and energy saving of underwater sensor network for monitoring seabed landslide. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 29–34. Coconut Creek (Florida), ISSN 0749-0208.

The traditional positioning system of underwater sensor network for monitoring submarine landslide could complete the location of submarine landslide, but the positioning offset was large and it was unable to work for a long time. Therefore, the research on positioning and energy saving of underwater sensor network for monitoring submarine landslide was proposed. The gradient of water depth was calculated based on multibeam bathymetric data, and the gradient change was recognized. Combined with topography features, the scarp of submarine landslide was recognized, and the identification of position of submarine landslides was determined. Based on SLMP positioning algorithm, the mechanism of UPS framework was used to realize the accurate coordinate positioning of submarine landslide. DCT transform, LDPC coder-decoder and Key frame coder-decoder of encoding algorithm of underwater sensor were used to transform Wyner-Ziv domain code and thus to realize energy-saving operation of underwater sensor network monitoring. Experiment proves that the positioning accuracy of method in this paper is improved by 58.33%, and the energy can be saved more than 55.75%. Meanwhile, it has high efficiency.

©Coastal Education and Research Foundation, Inc. 2018
Yan Xu, Hongxian Shan, and Yonggang Jia "Location and Energy Saving of Underwater Sensor Network for Monitoring Seabed Landslide," Journal of Coastal Research 83(sp1), 29-34, (4 May 2019). https://doi.org/10.2112/SI83-006.1
Received: 9 October 2017; Accepted: 5 February 2018; Published: 4 May 2019
KEYWORDS
network monitoring
recognition model
submarine landslide
Underwater sensor
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