SOM Based Multi-agent Hydro Meteorological Data Collection System

  • Gediminas Gricius
  • Darius Drungilas
  • Arunas Andziulis
  • Dale Dzemydiene
  • Miroslav Voznak
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 289)


The paper presents the possibilities of development the hydro meteoro-logical data collection system (HMDCS) involving advanced technologies such as multi agent based interaction and data collection between several monitoring system’s nodes (i.e. buoys) based on self-organizing maps (SOM). The require-ments for such system development are rather complex and are attached to allow-ing the real-time monitoring, control, and prediction of the negative consequences of contamination of surface water recourses and making their evaluation by effec-tiveness in monitoring of Baltic Sea surface water. The experiment is based on the design an inexpensive, but reliable Baltic Sea autonomous monitoring network (buoys), which would be able, continuously monitor and collect temperature, waviness, and other required data. Moreover, it makes ability to monitor all the data from the costal-based station with limited transition speed by setting different tasks for agent based buoy system according to the SOM.


wireless networking system hydro meteorological sensors multi agent systems embedded systems 


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  1. 1.
    Barrera, C., Rueda, M.J., Elgue, J.C., Llinas, O.: Red ACOMAR: Coastal Moored Buoy Network for Real-Time Surveillance, Control and Observation in Canary Islands. In: OCEANS 2006, pp. 1–5 (2006)Google Scholar
  2. 2.
    Bender, L., Guinasso, N.: A Comparison of Methods for Determining Significant Wave Heights. Journal of Atmospheric and Oceanic Technology 27, 1012–1028 (2009)CrossRefGoogle Scholar
  3. 3.
    Bielskis, A.A., Andziulis, A., Ramasauskas, O., Guseinoviene, E., Dzemydiene, D., Gricius, G.: Multi-Agent Based E-Social Care Support System For Inhabitancies of a Smart Eco-Social Apartment. Electronics and Electrical Engineering, pp. 11–14. Technologija, Kaunas (2011)Google Scholar
  4. 4.
    Bykovas, D., Drungilas, D., Andziulis, A., Venskus, J.: Jūrų Tyrimų ir Monitoringo Sen-sorinės Automatizuotos Informacinės Sistemos, Skirtos Išankstiniam Ekologinių Problem Identifikavimui, Projektavimo Koncepcija. Jūros ir krantų tyrimai - 2013: konferencijos medžiaga. 7-oji nacionalinė jūros mokslų ir technologijų konferencija, 2013 balandžio 3-5 / Klaipėdos Universiteto Baltijos pajūrio aplinkos tyrimų ir planavimo institutas, pp. 39–42. KU, Klaipėda (2013)Google Scholar
  5. 5.
    Collins, C.: In Situ Wave Measurements: Sensor Comparison and Data Analysis. Open Access Theses, University of Miami, vol. III, p. 372 (2012) Google Scholar
  6. 6.
    Drungilas, D., Bielskis, A.A., Denisov, V.: An Intelligent Control System Based on Non-Invasive Man Machine Interaction. In: Innovations in Computing Sciences and Software Engineering, pp. 63–68 (2010) Google Scholar
  7. 7.
    Dzemydienė, D.: Sprendimų Paramos Sistemos Galimybės Vertinti Vandens Taršos Pro-cesus. Jūros ir krantų tyrimai - 2013 : konferencijos medžiaga. 7-oji nacionalinė jūros mokslų ir technologijų konferencija, 2013 balandžio 3-5 / Klaipėdos Universiteto Baltijos pa-jūrio aplinkos tyrimų ir planavimo institutas, pp. 69–72. KU, Klaipėda (2013) Google Scholar
  8. 8.
    Laarhuis, J.H.: MaritimeManet: Mobile ad-hoc networking at sea. In: 2010 International Water-Side Security Conference, pp. 1–6 (2010)Google Scholar
  9. 9.
    Li, T.L.T.: Multi-sink opportunistic routing protocol for underwater mesh network. In: 2008 International Conference on Communications, Circuits and Systems, pp. 405–409 (2008)Google Scholar
  10. 10.
    Londhe, S.N.: Development of Wave Buoy Network Using Soft Computing Techniques. In: OCEANS 2008 - MTS/IEEE Kobe Techno-Ocean, pp. 1–8 (2008)Google Scholar
  11. 11.
    Maxim Integrated: DS18B20, Datasheat (2008)Google Scholar
  12. 12.
    Mirza, M.A., Shakir, M.Z., Slim-Alouini, M.A.: GPS-free Passive Acoustic Localization Scheme for Underwater Wireless Sensor Networks. In: 2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems, pp. 879–884 (2011)Google Scholar
  13. 13.
    Panchang, V., Zhao, L., Demirbilek, Z.: Estimation Of Extreme Wave Heights Using GEOSAT Measurements. Ocean Engineering, 205–225 (1999)Google Scholar
  14. 14.
    Sieber, A., Cocco, M., Markert, J., Wagner, M.F., Bedini, R., Dario, P.: ZigBee based buoy network platform for environmental monitoring and preservation: Temperature profiling for better understanding of Mucilage massive blooming. In: 2008 International Workshop on Intelligent Solutions in Embedded Systems, pp. 1–14 (2008)Google Scholar
  15. 15.
    Texas Instruments: LM35 Precision Centigrade Temperature Sensors. Datasheat (2013)Google Scholar
  16. 16.
    International Council for the Exploration of the Sea, Baltic Sea monitoring data,

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Gediminas Gricius
    • 1
  • Darius Drungilas
    • 2
  • Arunas Andziulis
    • 2
  • Dale Dzemydiene
    • 1
  • Miroslav Voznak
    • 3
  1. 1.Institute of Mathematics and InformaticsVilnius UniversityVilniusLithuania
  2. 2.Department of Informatics Engineering, Faculty of Marine EngineeringKlaipeda UniversityKlaipedaLithuania
  3. 3.Department of Telecommunications, Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstravaCzech Republic

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