An Approach for Networking of Wireless Sensors and Embedded Systems Applied for Monitoring of Environment Data

  • Dalė DzemydienėEmail author
  • Vytautas Radzevičius
Part of the Studies in Computational Intelligence book series (SCI, volume 869)


The aim of this research is to provide an approach for extension of the capacities of wireless sensor network (WSN) by enabling possibilities of monitoring of sea environment parameters. This domain area is related with control of recognition process of the hydro meteorological situations by providing the proper structure of the decision support system (DSS), which can work under specific real-time conditions. The construction issues of such multilayered buoy’s infrastructure based on WSN causes some problems: mechanisms of such interconnected devices have to work by properly presented goals and in extreme weather conditions; the modern embedded systems requires the intellectualization possibilities with recognition of adequate evaluation of risky situations; the functionality of WSN has to be based on the technology of the Internet of Things (IoT) with quite limited capacities. Our main findings concern the proposed architectural solution with extended capacities of integrated embedded systems and other gathering sources, by enabling the assessing of water pollution situations. The originality of the proposed approach concerns the issues of network allocation, system resource allocation and the architectural solutions, enabling wireless channels to provide big streams of data. The experimental results show the possibilities of the proposed prototype of multilayer system to work under real conditions of sea with interconnected network of sensors and controllers, based on the principles of other standardized network layers. The prototype method is used for evaluation of functionality of the embedded systems and WSN. The provided experimental results show functional effectiveness of collection and gathering of data under restricted and limited capacities of the embedded systems for operative control needs.


Wireless sensor network (WSN) Decision support system (DSS) Internet of things (IoT) Embedded systems Monitoring data Sea water environment data 



Authors would like to express their gratitude to the Marine Research Institute and the Department of Marine Engineering of the Faculty of Marine Engineering and Natural Sciences of Klaipeda University for the supported conditions for experimenting with the system of distant buoys in the Baltic Sea.


  1. Baqar M, Sadef Y, Ahmad SR, Mahmood A, Li Y, Zhang G (2018) Organochlorine pesticides across the tributaries of River Ravi, Pakistan: human health risk assessment through dermal exposure, ecological risks, source fingerprints and spatial-temporal distribution. Sci Total Environ 618:291–305CrossRefGoogle Scholar
  2. Belikova V, Panchuk V, Legin E, Melenteva A, Kirsanov D, Legin A (2019) Continuous monitoring of water quality at aeration plant with potentiometric sensor array. J Sens Actuators B Chem 282:854–860CrossRefGoogle Scholar
  3. Dzemydiene D, Maskeliunas S, Dzemydaite G, Miliauskas A (2016) Semi-automatic service provision based on interaction of data warehouses for evaluation of water resources. Informatica 27(4):709–722CrossRefGoogle Scholar
  4. Gricius G, Drungilas D, Andziulis A, Dzemydiene D, Voznak M (2014) SOM based multi-agent hydro meteorological data collection system. In: Nostradamus 2014: prediction, modeling and analysis of complex systems. Springer, Berlin, pp 31–41Google Scholar
  5. Gricius G, Drungilas D, Andziulis A, Dzemydiene D, Voznak M, Kurmis M, Jakovlev S (2015) Advanced approach of multi-agent based buoy communication. Sci World J (2015):1–7Google Scholar
  6. Hart JK, Martinez K (2006) Environmental sensor networks: a revolution in the earth system science? Earth Sci Rev 78:177–191CrossRefGoogle Scholar
  7. Hoagland P, Scatasta S (2006) The economic effects of harmful algal blooms. In: Ecology of harmful algae. Springer, Berlin, pp 391–402 Google Scholar
  8. Jadhav PS, Deshmukh VU (2012) Forest fire monitoring system based on Zig-Bee wireless. Int J Emerg Technol Adv Eng 2(12):187–191Google Scholar
  9. Keshtgari M, Deljoo A (2012) A wireless sensor network solution for precision agriculture based on ZigBee technology. Wirel Sens Netw 4:25–30CrossRefGoogle Scholar
  10. Lewis FL (2005) Wireless sensor networks. In: Cook DJ, Das SK (eds) Smart environments: technologies, protocols, and applications. Wiley, New York, pp 13–46.
  11. Mardani A, Jusoh A, Zavadskas EK (2015) Fuzzy multiple criteria decision-making techniques and applications–Two decades review from 1994 to 2014. Expert Syst Appl 42(8):4126–4148. Scholar
  12. Medineckiene M, Zavadskas EK, Björk F, Turskis Z (2015) Multi-criteria decision-making system for sustainable building assessment/certification. Arch Civ Mech Eng 15(1):11–18CrossRefGoogle Scholar
  13. Transforming our World (2015) The 2030 Agenda for Sustainable Development. United Nations, New YorkGoogle Scholar
  14. Wade TJ, Calderon LJ, Brenner KP, Sams E, Beach M, Haugland R, Wymer R, Dufour AP (2008) High sensitivity of children to swimming-associated gastrointestinal illness: results using a rapid assay of recreational water quality. Epidemiology 19:375–383CrossRefGoogle Scholar
  15. Wang W, Chen J, Hong T (2018) Occupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings. Autom Constr 94:233–243CrossRefGoogle Scholar
  16. Zhi-Gang H, Cai-Hui C (2009) The application of ZigBee based wireless sensor network and GIS in the air pollution monitoring. In: Proceedings of international conference on environmental science and information application technology, vol 2. WuhanGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Data Science and Digital TechnologiesVilnius UniversityVilniusLithuania

Personalised recommendations