Grey Analysis on Underwater Sensor Network of Penghu Set Net

  • Yih-Fuh Wang
  • Chang-Ling Tsai
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 297)


The set-net enables the fishermen to collect the fish alive without harming them. Since the net is located near the shore, the set-net serves as a fishing bank because fishermen do not need to go far to fish. The fish harvest can be sold as soon as captured alive and be sold as value-added as branded fish. In this paper, we apply a small-scale underwater sensor networks for collecting data by the ratio of the information on tides and catches and use grey theory to do analysis. It reveals that the grey theoretical is possible to be helpful for analysis of the relationship between tides and catches in set net. Besides, simulation result shows that the grey correlation can be a better auxiliary to allow operators and consumers making decision when to send boat for catching.


underwater sonar network grey analysis set net 


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  1. 1.
    Srinivas, S., Ranjitha, P., Ramya, R., Narendra, G.K.: Investigation of Oceanic Environment Using Large-Scale UWSN and UANETs. In: Proceedings of International Conference on Wireless Communications Networking and Mobile Computing, pp. 1–5 (2012)Google Scholar
  2. 2.
    Tomasi, B., Toso, G., Casari, P., Zorzi, M.: Impact of Time-Varying Underwater Acoustic Channels on the Performance of Routing Protocol. IEEE Journal of Oceanic Engineering 38(4), 772–784 (2013)CrossRefGoogle Scholar
  3. 3.
    Yu, C.H., Lee, K.H., Choi, J.W., Seo, Y.B.: Distributed Single Target Tracking in Underwater Wireless Sensor Networks. In: Proceedings of SICE Annual Conference 2008, pp. 1351–1356 (2008)Google Scholar
  4. 4.
    Xie, M., Xiao, X.: Grey decision rules for interval MADA based on rough set theory. In: Proceedings of Grey Systems and Intelligent Services, pp. 866–869 (2011)Google Scholar
  5. 5.
    Lin, C.C., Lin, J.F., Yu, C.C., Lee, T.Q.: Proceedings of Machine Learning and Cybernetics, pp. 2898–2903 (2010)Google Scholar
  6. 6.
    Ranganathan, S., Senthilvelan, T., Gopalakannan, S.: Multiple Performance Optimization in Drilling of GFRP Composites Using Grey Analysis. In: Proceedings of Advances in Engineering, Science and Management, pp. 12–18 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer Science and Information Engineering, Graduate Institute of Electrical Engineering and Computer ScienceNational Penghu University of Science and TechnologyPenghuTaiwan
  2. 2.Graduate Institute of Electrical Engineering and Computer ScienceNational Penghu University of Science and TechnologyPenghuTaiwan

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