Кафедра інженерії програмного забезпечення
Постійне посилання на фондhttps://repository.lntu.edu.ua/handle/123456789/77
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Item type:Наукова стаття, Latency Reduction in Real-time GPS tracking in Android and the Web-based GPS Monitoring System(IEEE, 2022-12-09) Tsindeliani, Davyd; Povstiana, Yuliia; Lishchyna, Nataliia; Yashchuk, AndriiThe paper focuses on the research aimed to reduce latency in real-time GPS-tracking and implementation of a real-time web GPS-monitoring system based on Android devices that publicly provides access to users' locations. A real-time GPS monitoring system with low latency consists of three main components: a web front-end client, a server part, and an Android application. During the development process, the performance of WebSocket back-end frameworks was researched. The performance of Google Maps and Leaflet (OpenStreetMap) was compared to select the option which provides the best performance in the developed system. The conducted research and the chosen solutions for the implementation of the project made it possible to develop a system that shows high efficiency and performance. The developed software allows any user with an Android mobile device to share their location with others with minimal latency and show their location and traffic history. © 2022 IEEE.Item type:Наукова стаття, Architecture And Experimental Evaluation Of A Cross-Platform Mobile Application For Adaptive Learning Using Large Language Models(Lutsk: LNTU, 2026-05-29) Povstiana, Yuliia; Samchuk, Lyudmila; Lishchyna, Nataliia; Boiko, LevThe paper addresses the design and experimental evaluation of the architecture of a cross-platform mobile application for adaptive foreign language learning using large language models. An architectural approach based on a dedicated AI Integration Layer is proposed, enabling the separation of business logic and improving the reliability of interaction with external AI services. The system implements adaptive content generation considering individual user characteristics. Special attention is given to performance optimization through the implementation of a multi-level caching mechanism, which reduced AI service usage costs by 74 % and decreased response latency. An experimental evaluation of system performance demonstrated stable operation under a load of up to 100 concurrent users with an average response time of 280-500 ms, and identified a degradation threshold at 160-170 users. The obtained results confirm the effectiveness of the proposed approach and its applicability for developing modern mobile educational systems based on large language models.