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Browsing Статті by Author "Shulakova K. S."

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    INVERSE AND DIRECT MAXFLOW PROBLEM STUDY ON THE FREE-ORIENTED ST-PLANAR NETWORK GRAPH
    (Anhalt University of Applied Sciences, 2023) Тіхонов В. І.; Tikhonov V. I.; Нестеренко С.; Nesterenko S.; Тагер А.; Taher A.; Тіхонова О. В.; Tikhonova O.; Цира О. В.; Tsyra O.; Яворська О. М.; Yavorska O.; Шулакова К. С.; Shulakova K. S.
    The issues of data flow optimization in telecommunication networks are considered. The analyses of the problem state of art shows the primarily utilization of logistic Maxflow model on ST-planar directed network graph with predetermined fixed metric. Concluded, that conventional logistic Maxflow model is not adequate to modern telecoms with flexibly reconfigured channels. Introduced the concept of the free-oriented network graph as an enhanced math-model for digital flows simulation. The inverse and direct Maxflow tasks are formulated on the normalised free-oriented ST-planar network graph, and the properties of the graph obtained as functions of vertices number.The direct Maxflow task is studied in tensor form, and the algorithm of test-sequences generation for the inverse Maxflow task is constructed. The inverse Maxflow problem has been analyzed as a discrete optimization task on the Pontryagin maximum principle with two necessary extremum conditions. Related computation algorithm is built with polynomial complexity. Unlike the known approaches, proposed method is relevant to data flow optimization in the software defined networks with dynamically reconfigurable channels. Along with the maximal flow, the flow distribution over the network structure provided. The formalism of the direct Maxflow task can be used for testing the algorithms of inverse Maxflow task solutions, and generation the training sequences for machine learning in AI models.
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    TURING MACHINE DEVELOPMENT FOR HIGH-SECURE DATA LINK ENCODING IN THE INTERNET OF THINGS CHANNEL
    (Anhalt University of Applied Sciences, 2024) Тіхонов В. І.; Tikhonov V.; Тагер А.; Taher A.; Тіхонов С.; Tikhonov V.; Шулакова К. С.; Shulakova K. S.; Глущенко В.; Glushchenko V.; Чайка А.; Chaika A.
    The work considers the issues of data stream encoding in the IoT systems and networks in the context of information security provision, with particular focus on the data link layer in wireless and wired telecommunication channels. The problem state of the art shows a great progress in the sphere of IT cybersecurity based on Advanced Encryption Standard. However, new tasks and related threats emerge, such as unmanned mobile devices of mass disposable with remote control, for which known approaches are not reliable and efficient enough. In this respect, the common algorithms of data link frame encoding are studied, and original formalization of typical frames proposed for static and dynamic representation. An original method introduced for high-secure data encoding with variable frame structure. A formal grammar of an abstract Turing machine (TM) is developed for data link encoding, which is based on ternary line-signaling and three-bit command system. Constructed typical frame-patterns with the use of TM syntax. General principles are formulated for the high-secure frame encoding with variable structure for packet-based streaming on the data link layer with the use of TM algorithm.The results of the work intend to improve the mobile objects cyber-threats protection, as well as to remote vehicle control and other IoT real-time applications.
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    USING A GENETIC ALGORITHM FOR TELEMEDICINE NETWORK OPTIMAL TOPOLOGY SYNTHESIS
    (Anhalt University of Applied Sciences, 2024) Царьов Р. Ю.; Tsaryov R.; Нікітюк Л. А.; Nikitiuk L.; Тимченко І.; Tymchenko I.; Кумиш В.; Kumysh V.; Шулакова К. С.; Shulakova K. S.; Сідень С. В.; Siden S. V.; Боднар Л.; Bodnar L.
    A method based on a genetic algorithm is proposed for synthesizing the optimal topological structure of telemedicine network, ensuring that the distribution of users (with a known location) by telemedicine stations (the number and location of which are also known) is optimal in terms of signal delay time during transmission and the cost of network deployment. The method uses: random generating of a base population, a tournament selection of chromosomes among two pairs for crossover, and a homogeneous crossover operator. The results of benchmarking the proposed method are presented. The experiment reveals that the resulting solution is indeed close to optimal, i.e. due to the use of a genetic algorithm, the method avoids falling into the trap of a local extremum. While the current study focused on a specific telemedicine network, future research could explore the scalability of this genetic algorithm approach for larger-scale networks and consider additional factors such as energy efficiency and fault tolerance.