Научный журнал «ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ И ТЕЛЕКОММУНИКАЦИИ»
Сообщение
2025, Vol. 13, Iss. 2 |
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E. Bagaev, P. Shalamov, G. Fokin
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Abstract: Purpose. Modern scenarios of high-precision positioning in wireless local area networks require overcoming the limitations associated with multipath propagation of signals and nonlinear delays. To implement the rangefinder positioning method in the absence of synchronization between the reference transceiver nodes, the well-known symmetrical double-sided two-way ranging (SDS-TWR) is used. The aim of the work is to study the application of symmetric two-way bidirectional distance measurement technology in the context of determining the location of a user device in a wireless LAN using nanoLOC technology with an unstable indoor environment. The novelty lies in the development of methodological support for the experimental assessment of the accuracy of positioning devices indoors using nanoLOC technology. The results show that using the nanoLOC system to solve the problem of determining the location of a user device can ensure measurement accuracy within a few decimeters through the use of SDS-TWR method. Practical relevance. The presented study can be used for the applied configuration of indoor location scenarios with the configuration of nanoLOC range measurement acquisition and processing modules, as well as for conducting laboratory classes on applied radio access systems.
Keywords: communications network, location detection systems, WLAN, user device, nanoLOC, ToA (time of arrival), ToF (time of flight), SDS-TWR (symmetric double-sided two way ranging).
DOI 10.31854/2307-1303-2025-13-2-1-31
EDN QEDDVK
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Ph. N. Hoang, A. Paramonov
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Abstract: Purpose. This article is dedicated to solving the problem of selecting an optimal data transmission strategy in heterogeneous Internet of Things (IoT) networks. To evaluate and select strategies, considering such conflicting factors as energy consumption, delay, and packet loss, an approach based on fuzzy multi-criteria analysis is proposed. This approach allows for finding effective trade-off solutions under conditions of uncertainty and fuzziness of the initial data, which are characteristic of IoT networks. Subject of research. Selection of a transmission strategy in heterogeneous IoT networks. Method: Fuzzy multi-criteria analysis, which enables the consideration and processing of multiple criteria and fuzzy data. Results. The effectiveness of the proposed approach for determining transmission strategies that provide an optimal balance between energy consumption, delay, and packet loss is demonstrated, which contributes to an increase in overall network performance. Practical significance. The developed approach is applicable for optimizing data transmission in real-world IoT networks, including reducing energy consumption and delays while maintaining a high probability of delivery, which is relevant for smart city and industrial automation applications.
Keywords: Internet of things, heterogeneous networks, transmission strategy, fuzzy analysis, multi-criteria optimization.
DOI 10.31854/2307-1303-2025-13-2-32-42
EDN ZUDPTT
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N. M. Redrugina , I. F. Tarabanov
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Abstract: The subject and purpose of the work. The article is devoted to solving the problem of load balancing in conditions of impatient users and heterogeneous traffic in telecommunication systems. The aim of the work is to develop a conceptual framework that combines analytical methods of queuing theory and modern machine learning approaches to minimize the cumulative penalty, taking into account maintenance delays and the cost of failures. The methods used. The research is based on the M/G/1/K analytical model with impatient users, which makes it possible to evaluate the key indicators of the system. For cases where an analytical solution is impossible or ineffective, the use of (1) time series forecasting to predict load, (2) binary classification to estimate the probability of outflow, and reinforcement learning to optimize the objective function is proposed. The novelty. The difference lies in a systematic approach to combining analytical and ML methods for balancing tasks, as well as taking into account the heterogeneous cost of failures for different traffic classes. A new formalization of the task is proposed through the prism of reinforcement learning. The main results. The concept of an intelligent balancing system has been developed, demonstrating potential advantages over traditional methods. Practical significance. The results can be used in the design of load management systems in autonomous networks.
Keywords: machine learning, queuing theory, load, mathematical modeling, migration, balancing.
DOI 10.31854/2307-1303-2025-13-2-43-51
EDN FMMVHK
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M. A. M. Al Sweity, Z. Kim, D. Marshev
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Abstract: Problem statement. With the growing volume of sensitive data and stricter requirements for their protection, traditional centralized machine learning methods are becoming unacceptable due to the risks of leaks and breaches of confidentiality. This problem is particularly acute in areas such as healthcare and finance, where the transfer of personal data to a central server is unacceptable. One of the promising solutions is federated learning, which allows global models to be trained without transferring source data, but maintaining a balance between model accuracy and privacy remains a key challenge. Methods. To solve the problem, an approach is proposed that combines the FedAvg aggregation algorithm with differential privacy mechanisms, including trimming gradients and adding Gaussian noise on the client side. Experimental validation was performed on the MNIST dataset using a convolutional neural network with various DP parameters. Results. With optimal settings (σ=0.5, ε≈3), 97.80% accuracy was achieved, which is only 1 % inferior to centralized training (98.79 %). Secure aggregation with 10 clients over 5 rounds showed an accuracy of 93.21 %. The analysis revealed a clear dependence of accuracy on privacy parameters, which allows you to flexibly customize the system to meet specific requirements. Practical significance. The proposed methodology provides a transparent and reproducible assessment of the “accuracy-privacy” compromise, which makes it applicable for implementation in real systems with sensitive data. The results can be used as a basis for adapting PHI in medical, financial, and other mission-critical applications where confidentiality is a priority.
Keywords: federated learning, differential privacy, machine learning, data protection, accuracy-privacy trade-off, secure aggregation.
DOI 10.31854/2307-1303-2025-13-2-52-68
EDN YHQXCK
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© SPbSUT © Authors
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