Skip to main content

Advertisement

SpringerLink
Log in

Design of containerized marine knowledge system based on IoT-Cloud and LoRaWAN

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

A Correction to this article was published on 13 April 2020

This article has been updated

Abstract

Recently, the research of marine knowledge and information technology has grown rapidly by the increasing interest in the rich repository of natural resources in the sea. For marine knowledge services, accurate marine environmental data must be continuously collected to deeply understand and analyze the marine circumstances. However, there is an insufficiency of research on the observation of marine circumstances in South Korea for the marine knowledge services. The ocean data buoy, a marine environmental monitoring equipment currently operating in South Korea, is large in size and high in production cost because it consumes a lot of power for communication. Aso, it provides only marine data and lacks information on marine knowledge. In this paper, we have proposed containerized marine knowledge system by means of IoT-Cloud and LoRaWAN to improve the marine environment monitoring. The proposed system enables flexible construction of the system and can analyze the marine knowledge through visualizing the gathered data and the knowledge processing with respect to the prediction of red tide events. LoRaWAN-based IoT devices are able to collect long-range marine environmental data in an energy efficient manner. Our proposed method is helpful for researching low-cost marine monitoring buoy and flexible marine knowledge system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Change history

References

  1. Park S, Lee SR (2014) Red tides prediction system using fuzzy reasoning and the ensemble method. Appl Intell 40(2):244–255

    Article  Google Scholar 

  2. J. Tateson, C. Roadknight, A. Gonzales, T. Khan, S. Fitz, I. Henning, N. Boyd, C. Vincent, and I. Marshall 2005 Real world issues in deploying a wireless sensor network for oceanography, in Proc. REALWSN

  3. Yoon NY, Namgung JI, Park HM, Park SH, Kim CH (2010) The underwater environment monitoring system based on ocean oriented WSN (wireless sensor network). J Korea Multimedia Soc 13(1):122–132

    Google Scholar 

  4. Yoon KH (2008) The present and future state of National Disaster Prevention System in Korea. J Korean Soc Environ Eng 30(2):128–135

    Google Scholar 

  5. You YH, Kang YS, Lee YB (2007) Development of a floating buoy for monitoring ocean environments. J Korean Soc Marine Eng 33(5):705–712

    Google Scholar 

  6. Lee YH, Kim SM, Kwon HJ, Kim JC (2016) Development of a gateway prototype for buoy systems. In: Proc. Winter 2016 KICS conference, pp 199–200

    Google Scholar 

  7. Rousseeuw K, Caillault EP, Lefebvre A, Hamad D (2015) Hybrid hidden Markov model for marine environment monitoring. IEEE J Sel Top Appl Earth Obs Remote Sens 8(1):204–213

    Article  Google Scholar 

  8. AI-Zaidi R, Woods JC, AI-Khalidi M, Hu H (2018) Building novel VHF-based wireless sensor networks for the internet of marine things. IEEE Sensors J 18(5):2131–2144

    Article  Google Scholar 

  9. Wang Y, Tan R, Xing G et al (2016) Monitoring aquatic debris using smartphone-based robots. IEEE Trans Mob Comput 15(6):1412–1426

    Article  Google Scholar 

  10. Perez CA, Valles FS, Sanchez RT et al (2017) Design and deployment of a wireless sensor network for the mar menor coastal observation system. IEEE J Ocean Eng 42(4):966–976

    Article  Google Scholar 

  11. Ibanez JAG, Morales LAG, Castillo JJC et al (2015) HYRMA: a hybrid routing protocol for monitoring of marine environments. IEEE Lat Am Trans 13(5):1562–1568

    Article  Google Scholar 

  12. Park S, Kim CW, Lee SR (2012) Marine environment monitoring and analysis system model. J KICS:2113–2120

  13. Park S, Cha BR, Kim JW (2017) Requirement analysis and concept design on software-defined hyper-converged marine devices. In: Proc. Winter KICS Conference, pp 133–133

    Google Scholar 

  14. E Hajric 2018 Knowledge management system and practices,” A Theoretical and Practical Guide for Knowledge Management in Your Organization

  15. Thierauf RJ (1999) Knowledge management systems. Quorum Books

  16. I Nonaka 1994 Theory of organizational knowledge creation, Organizational Science, Vol 5, No.1

  17. Megan S (2016) Mastering data mining with Python – find patterns hidden in your data. Packt Publishing

  18. F. Usama, P. S. Gregory and P. Smyth, The KDD process for extracting useful knowledge from volumes of data, 1996

    Google Scholar 

  19. J Han, M Kamber, and J Pei 2016 Data mining: concepts and techniques. Morgan Kaufmann

  20. B. R. Cha, S. Park, B. C. Shin and J. W. Kim, Design and verification of connected data architecture concept employing DataLake framework over Abyss storage cluster, Smart Media J Vol.7, No.3, Sep. 2018

  21. Song BH, Jung MA, Lee SR (2010) A design and implementation red tide prediction monitoring system using case based reasoning. J Korea Inf Commun Soc 35(12):1819–1826

    Google Scholar 

  22. Fdez-Riverola F, Corchado JM (2004) FSfRT: Forcasting system for red tides. Appl Intell 21:251–264

    Article  Google Scholar 

  23. Rong Z, Hong Y, Liping D (2006) Research on prediction of red tide based on fuzzy neural network. Marine Scence Bulletin 8(1):83–91

    Google Scholar 

  24. Kang I W, Park S, Lee Y W, Jeong M A, Oh I W (2011) Red tide prediction using neural network. In proceeding of 21st Joint conference on Communications and Information

  25. S Park, SR Lee, CC Park, HS Lim, JW Shin, JW Kwon 2011 Red tide blooms prediction using fuzzy reasoning, In proc. spring conference of Korea Information Processing Society

  26. S Park, SR Lee 2011 Red tide prediction using ensemble method, In proc. fall conference of Korea Information and Communications Society

  27. S Park, SR Lee 2011 Enhancing of red tide blooms prediction using fuzzy reasoning and naive Bayes classifier, In proc. summer conference of Korea Information and Communications Society

  28. Croce D, Gucciardo M, Mangione S, Santaromita G, Tinnirello I (2018) Impact of LoRa imperfect orthogonality: analysis of link-level performance. IEEE Commun Lett 22(4):796–799

    Article  Google Scholar 

  29. LoRa GPS Shield for Arduino, [Online] Available: http://www.dragino.com/products/lora/item/108-lora-gps-shield.html, Accessed on: 2019

  30. Dolinay J, Dostalek P, Vasek V (2016) Arduino debugger. IEEE Embed Syst Lett 8(4):85–88

    Article  Google Scholar 

  31. LG01-P LoRa Gateway, [Online] Available: http://www.dragino.com/products/lora/item/117-lg01-p.html, Accessed on: 2019

  32. Malik M, Neshatpour K, Rafatirad S, Homayoun H (2018) Hadoop workloads characterization for performance and energy efficiency optimizations on microservers. IEEE Trans Multi Scale Comput Syst 4(3):355–368

    Article  Google Scholar 

  33. JS Ma, HY Kim, YW Kim 2016 The virtualization and performance comparison with LXC-LXD in ARM64bit server, in Proc. International Conference on IT Convergence and Security, pp. 1–4

  34. Jin J, Song A, Gong H et al (2018) Distributed storage system for electric power data based on HBase. TUP J Magazines 1(4):324–334

    Google Scholar 

  35. Barve YD, Patil P, Bhattacharjee A, Gokhale A (2018) PADS: design and implementation of a cloud-based, immersive learning environment for distributed systems algorithms. IEEE Trans Emerg Top Comput 6(1):20–31

    Article  Google Scholar 

  36. S Prasad, SB Avinash 2013 Smart meter data analytics using OpenTSDB and Hadoop, in Proc. IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), pp. 1–6

  37. M Brattstrom, P Morreale 2017 Scalable agentless cloud network monitoring, in Proc. International Conference on Cyber Security and Cloud Computing (CSCloud), pp. 171–176

  38. Kim SH, Chung K (2016) Emergency situation monitoring service using context motion tracking of chronic disease patients. Clust Comput 18(2):747–759

    Article  Google Scholar 

  39. Jung H, Chung K (2016) Knowledge-based dietary nutrition recommendation for obese management. Inf Technol Manag 17(1):29–42

    Article  Google Scholar 

  40. Red Tide Information System, Korea National Institute of Fisheries Science. https://www.nifs.go.kr/redtideInfo. Accessed from 16 October to 12 November 2019

  41. Ocean Observation Data, Korea National Institute of Fisheries Science. http://www.nifs.go.kr/kodc/coo_list.kodc. Accessed from 16 October to 12 November 2019

  42. Korea National Weather Service. http://www.kma.go.kr/index.jsp. Accessed from 16 October to 12 November 2019

Download references

Funding

This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST) (2016R1D1A1B03934823).

Author information

Authors and Affiliations

  1. Artificial Intelligence Graduate School, GIST, Gwangju, 61005, South Korea

    Sun Park & JongWon Kim

  2. Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Teck Chaw Ling

  3. School of Electrical Engineering and Computer Science, GIST, Gwangju, 61005, South Korea

    ByungRea Cha

Authors
  1. Sun Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Teck Chaw Ling
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. ByungRea Cha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. JongWon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sun Park.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Park, S., Ling, T.C., Cha, B. et al. Design of containerized marine knowledge system based on IoT-Cloud and LoRaWAN. Pers Ubiquit Comput 26, 269–281 (2022). https://doi.org/10.1007/s00779-020-01381-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-020-01381-8

Keywords

Search

Navigation