Modular architecture of the intellectual system for solving the problems of the internet of things

Purpose of the study. The aim of the study is the formation of the modular architecture of an intelligent system for solving the problems of the Internet of things. For this, modern trends in the development of technologies are considered, among which the most significant, promising areas and events in the field of information technologies are highlighted. Among these areas, events highlighted: the introduction of Industry 4.0 technologies; use of cloud, fog computing; machine learning; cognitive approach in the field of artificial intelligence; development of cognitive computing. The main content of these areas is disclosed. In the context of the Internet of things, the modular architecture of an intelligent system is considered and its modules for solving tasks of purposeful behavior are described. As part of the development of the Internet of things, approaches to the management of innovative processes are considered. Emphasis is placed on the use of cognitive maps. With an increase in the volume of information, problems arise in making effective decisions. For this, intelligent systems focused on the use of conceptual representations and scripting concepts are considered.

Materials and research methods. When solving problems within the framework of the Industry 4.0 concept (the Internet of things), new approaches and methods are required. System Industry 4.0 represents many technologies, including the creation of devices, sensors, and many protocols for their interaction. One of its main directions is the Internet of things, which represents a new step in improving the Internet. For the development of the Internet of things, fog computing is used that extends and complements cloud computing. One of the central places on the Internet of things is machine learning. A brief description of the basic methods of machine learning is given. The use of a cognitive approach in the context of understanding and using mechanisms that allow a person to decipher information about reality and organize it for comparisons, decision making and many new tasks that arise in everyday life is shown. The developing directions of cognitive computing are presented.

Results. The modular architecture of an intelligent system for solving the problems of the Internet of things is proposed: modules of an intelligent system for solving the problems of the Internet of things using sensory images; intelligent system modules for solving the problems of the Internet of things using conceptual representations; intelligent system modules for solving the problems of the Internet of things using concept scenarios. Significant, promising areas and events in the field of information technology are presented and their main content is disclosed. Approaches to the management of innovative processes are considered. The use of a fuzzy cognitive map is described, which reflects the stages and factors of creating a robot vacuum cleaner that affect the life cycle.

Conclusion. Using the modular architecture of an intelligent system allows you to solve the problem of targeted behavior using sensual images of the world. Such systems can be used to solve the problems of the Internet of things. Using an integrated approach to the description of reality, the intelligent system of modular architecture allows the formation of conceptual representations and conceptual scenarios. They can reduce the flow of information, which is important for the Internet of things and the Internet of everything. Description of significant, promising areas in the field of information technology, approaches to managing innovative processes gives an understanding of the development of cognitive computing.

1. S. Rassel, P. Norvig Iskusstvennyy intellekt: sovremennyy podkhod, 2-ye izd.: Per. s angl = Norvig Artificial Intelligence: A Modern Approach, 2nd ed .: Tr. from Eng. Moscow: Williams Publishing House; 2007. 1408 p. (In Russ.)

2. Novikov O.YU Components of the concept Industry 4. 0 [Internet]. ITNOU: informatsionnyye tekhnologii v nauke, obrazovanii i upravlenii = ITNOU: information technologies in science, education and management. 2017; 1 (1). Available from: https://cyberleninka.ru/article/n/komponenty-ponyatiya-industry-4-0 (cited: 19.06.2019). (In Russ.)

3. «Industriya 4.0»: sozdaniye tsifrovogo predpriyatiya = «Industry 4.0»: the creation of a digital enterprise [Internet]. Available from: https://www.pwc.ru/ru/technology/assets/global_industry-2016_rus.pdf. (In Russ.)

4. H. Kagermann, W. Lukas and W. Wahlster Industrie 4.0: Mitdem Internet der Dinge auf dem Weg zur 4. Industriellen Revolution. VDI nachrichten. 2011. №13.

5. Lee E. A. Cyber Physical Systems: Design Challenges. 11th IEEE Symposium on Object Oriented Real-Time Distributed Computing (ISORC’08) Orlando, Florida. 2008. 363-369.

6. Giusto D. The Internet of Things. Iera A., Morabito G. and L. Atzori, Springer-Verlag New York; 2010. 442 p.

7. Kagermann H. Recommendations for implementing the strategic initiative Industrie 4.0: Final report of the Industrie 4.0 Working Group. W. Wahlster and J. Helbig; 2013.

8. Six technologies for Industry 4.0 [Internet]. Available from: https://www.gradiant.org/noticia/10173/?lang=en (cited 18.12.2016).

9. Industriya 4.0 [Internet]. Available from: http://www.sovtest.ru/industriya40/index.php (cited 18.12.2016). (In Russ.)

10. Salamatov I.A. Data localization through the use of cloud-fog technology [Internet]. Vestnik VUiT = Bulletin of VUiT. 2015; 1: 23. Available from: https://cyberleninka.ru/article/n/lokalizatsiya-dannyh-za-schet-ispolzovaniya-oblachno-tumannyh-tehnologiy (cited: 18.06.2019). (In Russ.)

11. Satton R.S. Barto E.G. Obucheniye s podkrepleniyem. Per. s angl = Reinforced training. Tr. from Eng. Moscow: BINOM. Laboratory of Knowledge; 2011. 399 p. (In Russ.)

12. Kuznetsov O.P. Cognitive semantics and artificial intelligence . Iskusstvennyy intellekt i prinyatiye resheniy = Artificial intelligence and decision making. 2012; 4: 32-42. (In Russ.)

13. Lakoff J. Women, Fire, and Dangerous Things: What Categories Reveal About the Mind. Chicago. University of Chicago Press; 1987.

14. Rosch E. Cognitive representations of semantic categories. Journal of Experimental Psychology. 1975; 104: 192–233.

15. Rybina G.V., Parondzhanov S.S. Tekhnologiya postroyeniya dinamicheskikh intellektual’nykh sistem: Uchebnoye posobiye = The technology of building dynamic intelligent systems: Tutorial. Moscow: NRNU MEPhI; 2011. 240 p. (In Russ.)

16. Trembach V.M. Intelligent system using concepts, concepts for solving problems of purposeful behavior [Internet]. Otkrytoye obrazovaniye = Open education. 2018; 22(1): 28-37. Available from: https://doi.org/10.21686/1818-4243-2018-1-28-37. (In Russ.)

17. Aleshchenko A.S., Trembach V.M., Trembach T.G. Distance learning systems and their development using cognitive mechanisms.[Internet]. Otkrytoye obrazovaniye = Open education. 2018; 22(5): 52-64. Available from: https://doi.org/10.21686/1818-4243-2018-5-52-64. (In Russ.)

18. Rothwell, R. Towards the fifth-generation innovation process. International Marketing Review. 1994; 11(1): 7-31.

19. Tebekin A.V., Tebekina A.A. The evolution of the development of models of the innovation process. Vestnik moskovskogo universiteta imeni S.YU. Vitte. Seriya 1. Ekonomika i upravleniye = Bulletin of Moscow University named after S.Yu. Witte. Series 1. Economics and Management. 2015; 3: 15-20. (In Russ.)

20. Medynskiy V. G. Innovatsionnyy menedzhment: uchebnik = Innovation management: a textbook. Moscow: INFRA-M; 2017. 295 p. (In Russ.)

21. Drogovoz P.A. Evolution of models of the innovation process and the modern classification of innovations. Kreativnaya ekonomika = Creative Economy. 2007; 1(7): 23-33. (In Russ.)

22. Tel’nov YU.F., Fedorov I.G. Inzhiniring predpriyatiy i upravleniye biznes-protsessami = Enterprise engineering and business process management. Moscow: Unity-Dana; 2015. 208 p. (In Russ.)

23. Vasil’yev V.I., Savina I.A., Sharipova I.I. The construction of fuzzy cognitive maps for the analysis and management of university information risks . Vestnik UGATU = Bulletin of USATU. 2008; 10; 2 (27): 199–209. (In Russ.)

24. Zayenchkovskiy A.E. Methods of modeling the logistics of innovations in the conditions of a difficultly formalized description of the external environment. Ekonomicheskiye nauki = Economic Sciences. 2011; 9: 145-149. (In Russ.)

25. Paklin N.B. Fuzzy-cognitive approach to the management of dynamic systems. Iskusstvennyy intellect = Artificial Intelligence. 2003; 4: 342-348. (In Russ.)

26. Trembach V.M. Cognitive approach to the creation of intelligent modules of organizational and technical systems. Otkrytoye obrazovaniye = Open Education. 2017; 2: 78-87. (In Russ.)

27. Trembach V.M. Resheniye zadach upravleniya v organizatsionno-tekhnicheskikh sistemakh s ispol’zovaniyem evolyutsioniruyushchikh znaniy: monografiya = Solving management problems in organizational and technical systems using evolving knowledge: a monograph. Moscow: MESI; 2010. 236 p. (In Russ.)

28. Karpov V.E., Karpova I.P., Kulinich A.A. Sotsial’nyye soobshchestva robotov: emotsii i temperament robotov; obshcheniye robotov; modeli kontagioznogo, podrazhatel’nogo i agressivnogo povedeniya robotov; komandnoye povedeniye robotov i obrazovaniye koalitsiy; prostranstvennaya pamyat’ animate = Social communities of robots: emotions and temperament of robots; communication robots; models of contagious, imitative and aggressive behavior of robots; team behavior of robots and the formation of coalitions; spatial memory of animat. Moscow: URSS: LENAND; 2019. 349 p. (Ser. «Sciences of the artificial»; No. 19). (In Russ.)