Intelligent traffic distribution model in cluster segments of heat technology systems

B.V. Okunev, E.K. Vereykina, A.I. Lazarev

---

Abstract. Nowadays decision-making on the management of heat technology systems is a rather complex process. The parameters direct expansion and interrelated elements among participants significantly affects the scaling of information flow assessment and control systems. Despite the rather rapid information and communication technologies development, the existing tools for organizing infrastructure support for the processes of interaction between the client and the server still have significant drawbacks. The imperfection of such solutions not only hinders the increasing of their effectiveness possibility, but also are vulnerabilities from the point of the security system functioning view as a whole. The purpose of the study is algorithmic and software develop of network segments flexible topology taking into account dynamically changing factors. As a result, an analysis of existing solutions for the construction of modular network protocols for the organization of the functioning of complex systems is carried out. Their advantages, vulnerabilities and potential sources of efficiency growth have been identified, which the proposed solution is aimed at their improvement and elimination. A model of a secure network of remote interaction and exchange of critical information is built to ensure stable operation of complex technical equipment in a heat technology system. A special feature of the developed model is the module of secure access to the required information due to direct p2p exchange between clients using a secure tunnel. The practical significance lies in the possibility of using the developed model of intelligent traffic distribution in the network segments of heat technology systems of various types of economic activity.

Keywords: TCP/IP topologies, data security, data management, neural models, thermal technology complexes

For citation. Okunev B.V., Vereykina E.K., Lazarev A.I. Intelligent traffic distribution model in cluster segments of heat technology systems. News of the Kabardino-Balkarian Scientific Center of RAS. 2023. No. 6(116). Pp. 235–246. DOI: 10.35330/1991-6639-2023-6-116-235-246

References

1. Kensworth S., Saumitra A., Vahid D. et al. On the Design and Implementation of IP-over-P2P Overlay Virtual Private Networks. IEICE Transactions on Communications. 2020. Vol. E103.B. No. 1. P. 2–10. DOI: https://doi.org/10.1587/transcom.2019CPI0001
2. Zhang Y., Zhong N., You W. et al. NDFuzz: a non-intrusive coverage-guided fuzzing framework for virtualized network devices. Cybersecurity. 2022. No. 5(21). DOI: https://doi.org/10.1186/s42400-022-00120-1
3. Seneviratne P. Beginning LoRa Radio Networks with Arduino: Build Long Range, Low Power Wireless IoT Networks. New York: Apress, 2019. 320 p.
4. Ahmadi A.E. An Introduction to Wireless Mesh Networks. New York: Scholars’ Press. 68 p.
5. Kim J.-W., Kim J., Lee J. Cross-Layer MAC/Routing Protocol for Reliability Improvement of the Internet of Things. Sensors. 2022. Vol. 22(9429). DOI: https://doi.org/10.3390/s22239429
6. Moiseev V.I. Eksperimental’noe issledovanie struktury paketnogo bufera Ethernet kommutatora [Experimental evaluation of ethernet switch packet buffer structures]. T-Comm. 2020. No. 1. Pp. 18–24. (In Russian)
7. Kanaev A.K., Login E.V., Grishanov I.S. Complex Algorithm for Control and Management Processes of Carrier Ethernet Telecommunication Network Using OAM Mechanisms. Proceedings of Petersburg Transport University. 2022. Vol. 19. No. 2. Pp. 266–275. DOI: 10.20295/1815-588X-2022- 2-266-275. (In Russian)
8. Nikishin K.I. Modeling a wireless sensor network using OMNET. Vestnik Ryazanskogo gosudarstvennogo radiotekhnicheskogo universiteta [Bulletin of Ryazan State Radio Engineering University]. 2022. No. 5. Pp. 85–90. (In Russian)
9. Simla J.A., Chakravarthy R., Leo M.L. An Experimental study of IoT-Based Topologies on MQTT protocol for Agriculture Intrusion Detection. Measurement: Sensors. 2022. Vol. 24. DOI: https://doi.org/10.1016/j.measen.2022.100470
10. Wang M., Li Y., Lv J., Gao Y., Qiao C., Liu B., Dong W. ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network. Electronics. 2022. No. 11(113). DOI: https://doi.org/10.3390/electronics11010113
11. Andreev S.V., Khlupina A.A. Optimizing speed for VPN providing the possibility of telework using routers powered by ARM CPU. Programmnye produkty i sistemy. Software & Systems. 2020. No. 4. Pp. 605–612. (In Russian)
12. Martyanov A.V. Analysis of information about connections to the enterprise network of remote users. Innovacionnaya nauka [Innovative science]. 2021. No. 6. Pp. 46–48. (In Russian)
13. Zaenchkovsky A., Kirillova E., Zeman Z. Mathematical foundations intellectually coordination of data for group expert innovative processes evaluation within the framework of scientific and industrial cooperation. Algorithms and solutions based on computer technology. Lecture Notes in Networks and Systems. Vol. 387. Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-93872-7_9
14. Dai Y., Zhou Q., Leng M. et al. Improving the bi-LSTM model with XGBoost and attention mechanism: A combined approach for short-term power load prediction. Applied Soft Computing. 2022. No. 130. DOI: https://doi.org/10.1016/j.asoc.2022.109632
15. Borisov V.V., Bulygina O.V., Dli M.I., Kozlov P.Yu. Rubrication of text documents based on fuzzy difference relations. Prikladnaya informatika [Journal of Applied Informatics]. 2020. Vol. 15. No. 3. Pp. 36–45. DOI: 10.37791/2687-0649-2020-15-3-36-45. (In Russian)
16. Pajankar A., Joshi A. Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch. New York: Apress, 2022. 356 p.
17. Chumakova E.V., Korneev D.G., Gasparian M.S. An approach to the design of a neural network for the formation of an individual trajectory of knowledge testing. Prikladnaya informatika [Journal of Applied Informatics]. 2022. Vol. 17. No. 5. Pp. 102–115. DOI: 10.37791/2687-0649-2022-17-5-102-115. (In Russian)
18. Nepomnyashchiy O.V. Resource estimation method in the process of functional-flow highlevel VLSI synthesis. Prikladnaya informatika [Journal of Applied Informatics]. 2022. Vol. 17. No. 3. Pp. 34–44. DOI: 10.37791/2687-0649-2022-17-3-34-44. (in Russian)
19. Park M.-H., Chakraborty S., Vuong Q.D. et al. Anomaly detection Babed on time series data of hydraulic accumulator. Sensors. 2022. Vol. 22(9428). DOI: https://doi.org/10.3390/s22239428
20. Chen G., Tat T.P. Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems. 1st ed. Boca Raton: CRC Press, 2019. 328 p.

Information about the authors

Okunev Boris Vasilievich, Candidate of Technical Sciences, Associate Professor, Teacher of the Department of Information Technologies in Economics and Management, National Research University “Moscow Energy Institute” – branch in Smolensk;
214013, Russia, Smolensk, 1 Energetichesky proezd;
ok-bmw@rambler.ru, ORCID: https://orcid.org/0000-0001-8740-7855
Vereykina Elizaveta Konstantinovna, Graduate student of the National Research University “Moscow Energy Institute”;
111250, Russia, Moscow, 17 Krasnokazarmennaya street;
vereikina.ek@mail.ru, ORCID: https://orcid.org/0000-0003-3791-9099
Lazarev Alexey Igorevich, Assistant of the Department of Information Technologies in Economics and management, National Research University “Moscow Energy Institute” – branch in Smolensk;
214013, Russia, Smolensk, 1 Energetichesky proezd;
anonymous.prodject@gmail.com, ORCID: https://orcid.org/0000-0003-3252-0409