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Network of Autonomous Units for the Complex Technological Objects Reliable Monitoring

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Part of the Studies in Computational Intelligence book series (SCI, volume 976)

Abstract

Nowadays autonomous remote monitoring and control systems are required for the reliable operation of complex, especially distributed over large areas or inaccessible places, technological facilities and systems for various purposes (agriculture, urban studies, environmental protection, emergency natural and man-made situations, etc.), as well as for the operational technological or managerial decisions. To monitor the state of an object, as a rule, a distributed stationary system of sensors for various purposes is used. In this case, it is necessary to take into account the balance between functionality and cost of the system in each specific case. By functionality we mean not only a set of capabilities, but also energy consumption, which should be as low as possible for remote monitoring, performance, in particular, when processing camera images and transmitting data on the network, and protection from external interventions, both objective (technical obstacles), and malicious. The speed of the received data and its completeness remains a problem when using stationary systems. The solution may increase stationary points, but at the same time the network efficiency will decrease. Authors consider the relevant and perspective IIoT (Industrial Internet-of-Things) development concept—a scalable heterogeneous network consisting of fixed and mobile nodes for monitoring the state of complex distributed technological objects. Many issues must be solved comprehensively at designing and creating such a network. This is especially true for control systems, data transmission channels and data stream processing, their analysis, scalability, decision making. The paper describes a new concept for development of a multi-level architecture IoT network for monitoring the state of geographically distributed technological objects, consisting of a heterogeneous set of nodes (stationary and mobile units) equipped with various sensors and video cameras.

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References

  1. 1.
    Mosyagin, A.A.: Monitoring of potentially dangerous objects based on logical and probabilistic modeling. Abstract of dissertation research for the degree of candidate of technical sciences. M: Academy of the Ministry of Internal Affairs, 27p. (2009) (in rus.)Google Scholar
  2. 2.
    Solozhentsev, E.D.: Scenario Logic-Probabilistic Risk Management in Business and Technology. SPb. Publishing house “Business-Press”, 432p. (2004) (in rus.)Google Scholar
  3. 3.
    Tkachenko, T.E.: Monitoring of industrial objects as the basis for the prevention of technogenic emergencies. Sci. Educ. Probl. Civ. Prot. 1, 62–65 (2013) (in rus.)Google Scholar
  4. 4.
    Predictive emission monitoring systems monitoring emissions from industry. ABB Meas. Anal. ABB, 8p. (2019)Google Scholar
  5. 5.
    Trivedi, R., Vora, V.: Real-time monitoring and control system for industry. IJSRD – Int. J. Sci. Res. Dev. 1(2), 142–147 (2013). ISSN (online): 2321-0613Google Scholar
  6. 6.
    Russell, J.: Facebook is reportedly testing solar-powered internet drones again — this time with Airbus. TechCrunch. https://techcrunch.com/2019/01/21/facebook-airbus-solar-drones-internet-program/. Accessed 30 May 2019
  7. 7.
    UAVIA releases its “Uavia Inside” program for drone solutions providers. Paris, France, 07 May 2019. https://www.uavia.eu/PR_20190506_UAVIA_INSIDE
  8. 8.
    Kharchenko, V., Yastrebenetsky, M., Fesenko, H., Sachenko, A., Kochan, V.: NPP post-accident monitoring system based on unmanned aircraft vehicle: reliability models. Nucl. Radiat. Saf. 4(76), 50–55 (2017)Google Scholar
  9. 9.
    Sachenko, A., Kochan, V., Kharchenko, V., Yastrebenetsky, M., Fesenko, H., Yanovsky, M.: NPP post-accident monitoring system based on unmanned aircraft vehicle: concept, design principles. Nucl. Radiat. Saf. 1(73), 24–29.  https://doi.org/10.32918/nrs.2017.1(73).04
  10. 10.
    Younana, M., Housseina, E.H., Elhoseny, M., Alia, A.A.: Challenges and recommended technologies for the industrial internet of things: a comprehensive review. Measurement 151 (2020).  https://doi.org/10.1016/j.measurement.2019.107198
  11. 11.
    Grösser, S.N.: Complexity management and system dynamics thinking. In: Grösser, S., Reyes-Lecuona, A., Granholm, G. (eds.) Dynamics of Long-Life Assets. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-45438-2_5
  12. 12.
    Schott, P., Lederer, M., Eigner, I., Bodendorf, F.: Case-based reasoning for complexity management in Industry 4.0. J. Manuf. Technol. Manag.  https://doi.org/10.1108/jmtm-08-2018-0262
  13. 13.
    Duffy, V.G.: Handbook of Digital Human Modeling: Research for Applied Ergonomics and Human Factors Engineering. CRC Press, 1006p. (2016)Google Scholar
  14. 14.
    da Cruz, P.M.A.M.: Semantic figurative metaphors in information visualization. Coimbra: [s.n.]. Tese de doutoramento. Disponível na (2016). http://hdl.handle.net/10316/31166
  15. 15.
    Bushma, A.V., Turukalo, A.V.: Software controlling the LED bar graph displays. Semicond. Phys. Q. Electron. Optoelectron. 23(3), 329–335 (2020).  https://doi.org/10.15407/spqeo23.03.329
  16. 16.
    Connell, J., Fan, Q., Gabbur, P., Haas, N., Pankanti, S., Trinh, H.: Retail video analytics: an overview and survey. Proc. SPIE – Int. Soc. Opt. Eng. 8663 (2013).  https://doi.org/10.1117/12.2008899
  17. 17.
    Olatunji I.E., Cheng, C.-H.: Video analytics for visual surveillance and applications: an overview and survey. In: Tsihrintzis, G., Virvou, M., Sakkopoulos, E., Jain, L. (eds.) Machine Learning Paradigms. Learning and Analytics in Intelligent Systems, vol 1. Springer, Cham.  https://doi.org/10.1007/978-3-030-15628-2_15
  18. 18.
    EXCLUSIVE: Drones vulnerable to terrorist hijacking, researchers say [Electronic resource]. – Mode of access: http://www.foxnews.com/tech/2012/06/25/drones-vulnerable-to-terrorist-hijacking-researchers-say/ – Date of access: 15.05.2015
  19. 19.
    Davison, A.J., et al.: MonoSLAM: real-time single camera slam. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1052–1067 (2007)Google Scholar
  20. 20.
    Larson, C.D.: An integrity framework for image-based navigation systems. In: Larson, C.D. (ed.) Air Force Inst Of Tech Wright-Patterson Afb Oh School of Engineering and Management, vol. AFIT/DEE/ENG/10-03 (2010)Google Scholar
  21. 21.
    Robertson, D., Cipolla, R.: An image-based system for urban navigation. In: The 15th British Machine Vision Conference (BMVC04), pp. 819–828 (2004)Google Scholar
  22. 22.
    Roumeliotis, S.I., Johnson, A.E., Montgomery, J.F.: Augmenting inertial navigation with image-based motion estimation. In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA’02, vol. 4, pp. 4326–4333 (2002)Google Scholar
  23. 23.
    Templeton, T.: Autonomous vision-based landing and terrain mapping using an mpc-controlled unmanned rotorcraft In: IEEE International Conference on Robotics and Automation, Roma, 10–14 April 2007, pp. 1349–1356 (2007)Google Scholar
  24. 24.
    Taketomi, T., Uchiyama, H., Ikeda, S.: Visual SLAM algorithms: a survey from 2010 to 2016. IPSJ T Comput. Vis. Appl. 9, 16 (2017).  https://doi.org/10.1186/s41074-017-0027-2CrossRefGoogle Scholar
  25. 25.
    Huang, B., Zhao, J., Liu, J.: A survey of simultaneous localization and mapping with an envision in 6G wireless networks (2019)Google Scholar
  26. 26.
    Pizarro, D., Marron, M., Peon, D., Mazo, M., Garcia, J.C., Sotelo, M.A., Santiso, E.: Robot and obstacles localization and tracking with an external camera ring. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2008), Pasadena, CA, USA, 19–23 May 2008; pp. 516–521 (2008)Google Scholar
  27. 27.
    Ji, Y., Yamashita, A., Asama, H.: Automatic calibration and trajectory reconstruction of mobile robot in camera sensor network. In: Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE), Gothenburg, Sweden, 24–28 August 2015, pp. 206–211 (2015)Google Scholar
  28. 28.
    Pizarro, D., Santiso, E., Mazo, M., Marron, M.: Pose and sparse structure of a mobile robot using an external camera. In: Proceedings of the IEEE International Symposium on Intelligent Signal Processing (WISP 2007), Alcala de Henares, Spain, 3–5 October 2007, pp. 1–6 (2007)Google Scholar

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© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Pukhov Institute for Modelling in Energy Engineering NAS of UkraineKievUkraine
  2. 2.Borys Grinchenko Kyiv UniversityKievUkraine
  3. 3.United Institute of Informatics Problems of The National Academy of Sciences of BelarusMinskBelarus

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