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Published June 30, 2021 | Version v1
Journal article Open

Development of a model of a subsystem for forecasting changes in data transmission routes in special purpose mobile radio networks

Creators

  • 1. Institute of Special Communications and Information Protection of National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"

Description

This research addressed the issue of improving the quality of service for the control system of mobile radio networks. The analysis of the forecasting sphere concerning the methods of service quality of mobile radio networks for special purposes, in particular, forecasting the time of congestion of data transmission routes is carried out. It is found that these methods are used in wired and computer networks operating at the network and data link levels. The basic parameters of the protocols of the channel and network layers of mobile radio networks are highlighted. Forecasting methods are analyzed: temporal extrapolation, causality, expert, and the main disadvantages are indicated. A model of a control system for mobile radio networks with a forecasting subsystem is shown. The features of mobile radio networks, which form the requirements for routing methods, are described. A lot of requirements have been put forward for the model of a control system for mobile radio networks. The structure of a model of a control system for mobile radio networks with an improved forecasting subsystem is proposed. On the basis of genetic algorithms, the tasks that arise in the process of identification, training and forecasting in the forecasting subsystem are solved. The operation of the processes consists in building a base of rules aimed at identifying significant dependencies in a time series based on the use of a genetic algorithm. It is based on the use of evolutionary principles to find the optimal solution. Application of the proposed model will allow real-time identification and will significantly improve the quality of service for mobile radio networks. It will increase the speed and volume of data processed during training, improve the quality and reliability of predicting changes in data transmission routes

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References

  • Desai, R. M., Patil, B. P., Sharma, D. P. (2017). Learning Based Route Management in Mobile Ad-Hoc Networks. Indonesian Journal of Electrical Engineering and Computer Science, 7 (3), 718. doi: https://doi.org/10.11591/ijeecs.v7.i3.pp718-723
  • Salnyk, S., Hol, V., Divicky, A. (2020). Analysis of methods of data flow management in mobile radio networks of special purpose. Spetsialni telekomunikatsiyni systemy ta zakhyst informatsiyi, 1 (7), 41–51.
  • Salnik, V., Salnik, S., Lukina, K., Oleksenko, V. (2017). Analysis of methods of supporting decisions in automated military control management systems. Systemy ozbroiennia i viiskova tekhnika, 2 (50), 114–119.
  • Bovda, E. (2018). Model of the telecommunication network monitoring and forecasting with the use of urban neural networks. Zbirnyk naukovykh prats VITI, 1, 6–16.
  • Bagirov, S. R. (2017). Debatable questions of the ascertainment causal nexus and guilty in negligent mediated infliction a criminal consequence (by way of example Chilikov's and Maslov's case). Visnyk asotsiatsiyi kryminalnoho prava Ukrainy, 1 (8), 100–116.
  • Klymenko, N. I., Kalinina, I. V. (2019). Criminal And Expert Forecast: Issue Matters. Scientific Journal of Public and Private Law, 1 (1), 206–210. doi: https://doi.org/10.32844/2618-1258.2019.1-1.35
  • Goncharov, E. N., Leonov, V. V. (2017). Genetic algorithm for the resource-constrained project scheduling problem. Automation and Remote Control, 78 (6), 1101–1114. doi: https://doi.org/10.1134/s0005117917060108
  • Chauhan, D. V., Bhalani, D. K., Trivedi, I. N. (2018). Uluchshennyy VBLAST MAP: noviy algoritm tochka-tochka dlya detektirovaniya simvolov v sistemah besprovodnoy svyazi MIMO. Izvestiya vysshih uchebnyh zavedeniy. Radioelektronika, 61 (5), 259–266. doi: https://doi.org/10.20535/s0021347018050023
  • Vijayalakshmi, J., Prabu, K. (2018). Performance Analysis of Clustering Schemes in MANETs. Lecture Notes on Data Engineering and Communications Technologies, 808–813. doi: https://doi.org/10.1007/978-3-030-03146-6_92
  • Sudhakar, T., Hannah Inbarani, H., Senthil Kumar, S. (2019). Route classification scheme based on covering rough set approach in mobile ad hoc network (CRS-MANET). International Journal of Intelligent Unmanned Systems, 8 (2), 85–96. doi: https://doi.org/10.1108/ijius-08-2019-0046
  • Horn, A. L., Friedrich, H. (2019). The Network Source Location Problem in the Context of Foodborne Disease Outbreaks. Springer Proceedings in Complexity, 151–165. doi: https://doi.org/10.1007/978-3-030-14683-2_7
  • Periyasamy, J., Saravanan, R. (2018). Angle Based Energy and Power Efficient Node Detection Routing Protocol for MANET. Recent Patents on Computer Science, 11 (2), 134–142. doi: https://doi.org/10.2174/2213275911666180817120638
  • Dang, V. T., Huong, T. T., Thanh, N. H., Nam, P. N., Thanh, N. N., Marshall, A. (2018). SDN-Based SYN Proxy – A Solution to Enhance Performance of Attack Mitigation Under TCP SYN Flood. The Computer Journal, 62 (4), 518–534. doi: https://doi.org/10.1093/comjnl/bxy117
  • Tomar, S. S., Rawat, A., Vyavahare, P. D., Tokekar, S. (2020). Conceptual model for comparison of IPv6 ISPs based on IPv4 traffic profiles. International Journal of Information Technology, 12 (4), 1171–1182. doi: https://doi.org/10.1007/s41870-020-00453-5
  • Divicky, A., Borovyk, L., Salnyk, S., Hol, V. (2020). Analysis of methods for predicting changes in data transfer data in wireless self-organized networks. Scientific Works of Kharkiv National Air Force University, 1 (63), 60–67. doi: https://doi.org/10.30748/zhups.2020.63.08
  • Akyildiz, I. F., Lee, W.-Y., Chowdhury, K. R. (2009). CRAHNs: Cognitive radio ad hoc networks. Ad Hoc Networks, 7 (5), 810–836. doi: https://doi.org/10.1016/j.adhoc.2009.01.001
  • Sa-Iz, E., Poler, R., Andres, B. (2018). Intelligent Decision-support Systems in Supply Chains: Requirements Identification. Enterprise Interoperability, 23–29. doi: https://doi.org/10.1002/9781119564034.ch3
  • Kononiuk, A. Yu. (2008). Neironi merezhi i henetychni alhorytmy. Kyiv: «Korniychuk», 446.
  • Israr, A., Kaleem, M., Nazir, S., Mirza, H. T., Huss, S. A. (2020). Nested genetic algorithm for highly reliable and efficient embedded system design. Design Automation for Embedded Systems, 24 (4), 185–221. doi: https://doi.org/10.1007/s10617-020-09234-6
  • Hulianytskyi, L. F. (2012). Development of forecasting methods on the basis of evolutionary algorithms. Komp'yuternaya matematika, 1, 69–77. Available at: http://nbuv.gov.ua/UJRN/Koma_2012_1_10
  • Holland, J. H. (1984). Genetic Algorithms and Adaptation. Adaptive Control of Ill-Defined Systems, 317–333. doi: https://doi.org/10.1007/978-1-4684-8941-5_21