Universal controller for the distributed management in the adaptive smart home systems
| dc.citation.epage | 73 | |
| dc.citation.issue | 2 | |
| dc.citation.journalTitle | Український журнал інформаційних технологій | |
| dc.citation.spage | 64 | |
| dc.citation.volume | 6 | |
| dc.contributor.affiliation | Національний університет “Львівська політехніка” | |
| dc.contributor.affiliation | Lviv Polytechnic National University | |
| dc.contributor.author | Теслюк, В. М. | |
| dc.contributor.author | Береговська, Х. В. | |
| dc.contributor.author | Зербіно, Д. Д. | |
| dc.contributor.author | Теслюк, Т. В. | |
| dc.contributor.author | Сенета, М. Я. | |
| dc.contributor.author | Teslyuk, V. M. | |
| dc.contributor.author | Beregovska, Kh. V. | |
| dc.contributor.author | Zerbino, D. D. | |
| dc.contributor.author | Teslyuk, T. V. | |
| dc.contributor.author | Seneta, M. Ya. | |
| dc.coverage.placename | Львів | |
| dc.coverage.placename | Lviv | |
| dc.date.accessioned | 2025-11-19T08:26:01Z | |
| dc.date.created | 2024-02-27 | |
| dc.date.issued | 2024-02-27 | |
| dc.description.abstract | Розроблено структуру та алгоритм функціонування універсального контролера адаптивної системи “розумний дім”. Розподілене управління забезпечує підвищену живучість, а модульний принцип організації системи – ефективну модернізацію у майбутньому. Розроблена інформаційна модель передбачає зв’язок із віддаленим сервером даних за допомогою Wi–Fi-модуля на мікроконтролері ESP8266, який підтримує локальну Wi–Fi-мережу для забезпечення управління “розумним будинком” через смартфони. З локальною IP-адресою ESP8266 пов’язана вебсторінка, через яку користувачі отримують інформацію на смартфон про поточний стан будинку і можуть дистанційно керувати ним. Періодично через цей самий Wi–Fi-модуль система відправляє дані на хмарний сервер, а також зчитує з нього дані для дистанційного управління будинком. Підключення до Інтернету здійснюється через Wi–Fi роутер. Для ефективної організації обміну внутрішніми даними між контролерами в системі використано протокол двопровідної спільної шини I2C. Зв’язок між системним контролером і сервером здійснюється через універсальний канал UART. Сервер системи передає AT-команди та дані для модуля Wi–Fi ESP8266 через другий канал UART. Ціна пропонованого технічного рішення невисока. Програмне забезпечення універсальних системних контролерів розроблено на мові асемблера для мікроконтролера STM8, що дає змогу забезпечити високу швидкість роботи пристрою. Для реалізації “розумного дому” використано принципи інтелектуальної адаптації системи під користувача. Розроблено технічну підтримку адаптивної системи розумного будинку, яка характеризується невисокою ціною. Під час розроблення враховано збереження максимально ефективного співвідношення: якість як вигода в часі до вартості впровадження системи. | |
| dc.description.abstract | It is developed the structure and algorithm of functioning of the universal controller of the adaptive smart home system. Distributed management provides increased survivability, and the modular principle of system organization ensures effective modernization in the future. The developed information model involves a connection with a remote data server using a Wi–Fi module on an ESP8266 microcontroller, which supports a local Wi–Fi network to provide control of a smart home through smartphones. A web page is associated with the local IP address of the ESP8266, through which users receive information on the current state of the home on their smartphone and can control it remotely. Periodically, through the same Wi–Fi module, the system sends data to the cloud server, as well as reads data from it for remote control of the home. The connection to the Internet is made through a Wi–Fi router. The system performs simple and urgent operations without the server involvement. Depending on the needs, the universal controller can be replaced with a specialized one. For efficient organization of the exchange of internal data between controllers, the protocol of two–wire shared bus I2C is used in the system. Communication between the system controller and the server is carried out via the universal UART channel. The system server transmits AT commands and data for the ESP8266 Wi–Fi module via the second UART channel. The proposed technical solution is characterized by a low price. The developed software for universal system controllers is developed in the assembly language for the STM8 microcontroller, which ensures high-speed operation of the device. Examples of the layout of the “transmitter” of the system and the implementation of the “receiver” of the adaptive smart home system are considered. The hardware and software structure for controller development for distributed management in the adaptive smart home systems is proposed. The principles of intelligent adaptation of the system to the user were used to implement a smart home. The technical support of the adaptive system of the smart house is developed, which is characterized by a low price. The creation of such adaptive systems can be implemented with different “levels” of intelligence. During development, it is very important to maintain the maximum ratio: of quality as an approximate benefit in time, which saves the user of the system, to the cost of system implementation. | |
| dc.format.extent | 64-73 | |
| dc.format.pages | 10 | |
| dc.identifier.citation | Universal controller for the distributed management in the adaptive smart home systems / V. M. Teslyuk, Kh. V. Beregovska, D. D. Zerbino, T. V. Teslyuk, M. Ya. Seneta // Ukrainian Journal of Information Technology. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 6. — No 2. — P. 64–73. | |
| dc.identifier.citationen | Universal controller for the distributed management in the adaptive smart home systems / V. M. Teslyuk, Kh. V. Beregovska, D. D. Zerbino, T. V. Teslyuk, M. Ya. Seneta // Ukrainian Journal of Information Technology. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 6. — No 2. — P. 64–73. | |
| dc.identifier.doi | doi.org/10.23939/ujit2024.02.064 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/120434 | |
| dc.language.iso | en | |
| dc.publisher | Видавництво Львівської політехніки | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Український журнал інформаційних технологій, 2 (6), 2024 | |
| dc.relation.ispartof | Ukrainian Journal of Information Technology, 2 (6), 2024 | |
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| dc.relation.referencesen | 1. Berezsky, O., Verbovyy, S., & Pitsun, O. (2018). Hybrid intelligent information technology for biomedical image processing. In Proceedings of the IEEE International Conference of Computer Science and Information Technologies (pp. 420-423). Lviv. https://doi.org/10.1109/STC-CSIT.2018.8526711 | |
| dc.relation.referencesen | 2. Molnár, E., Molnár, R., Kryvinska, N., & Greguš, M. (2014). Web intelligence in practice. Journal of Service Science Research, 6(1), 149-172. https://doi.org/10.1007/s12927-014-0006-4 | |
| dc.relation.referencesen | 3. Boreiko, O. Y., & Teslyuk, V. M. (2016). Developing a controller for registering passenger flow of public transport for the "smart" city system. Eastern-European Journal of Enterprise Technologies, 6(3), 40-46. https://doi.org/10.15587/1729-4061.2016.84143 | |
| dc.relation.referencesen | 4. Lytvyn, V., Vysotska, V., Mykhailyshyn, V., Peleshchak, I., Peleshchak, R., & Kohut, I. (2019). Intelligent system of a smart house. In 3rd International Conference on Advanced Information and Communications Technologies (pp. 282-287). https://doi.org/10.1109/AIACT.2019.8847748 | |
| dc.relation.referencesen | 5. Saheed, Y. K., & Arowolo, M. O. (2021). Efficient cyber attack detection on the Internet of medical things-smart environment based on deep recurrent neural network and machine learning algorithms. IEEE Access, 9, 161546-161554. https://doi.org/10.1109/ACCESS.2021.3128837 | |
| dc.relation.referencesen | 6. Alrayes, F. S., Asiri, M. M., Maashi, M., Salama, A. S., Hamza, M. A., Ibrahim, S. S., Zamani, A. S., & Alsaid, M. I. (2023). Intrusion detection using chaotic poor and rich optimization with deep learning model for smart city environment. Sustainability, 15, 6902. https://doi.org/10.3390/su15086902 | |
| dc.relation.referencesen | 7. Teslyuk, V., Beregovska, Kh., Denysyuk, P., & Mashevska, M. (2017). Method of development Smart-House-Systems models, based on Petri-Markov nets, and extended by functional components. In Proceedings of the XIIth International Conference of Computer Science and Information Technologies (pp. 352-355). Lviv: Publishing House Vezha&Co. https://doi.org/10.1109/STC-CSIT.2017.8098803 | |
| dc.relation.referencesen | 8. Gram-Hanssen, K., & Darby, S. J. (2018). Home is where the smart is? Evaluating smart home research and approaches against the concept of home. Energy Research & Social Science, 37, 94-101. https://doi.org/10.1016/j.erss.2017.09.037 | |
| dc.relation.referencesen | 9. Bernheim Brush, A. J., Lee, B., Mahajan, R., Agarwal, S., Saroiu, S., & Dixon, C. (2011). Home automation in the wild: Challenges and opportunities. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2115-2124). https://doi.org/10.1145/1978942.1979249 | |
| dc.relation.referencesen | 10. Radha, R. K. (2021). Flexible smart home design: Case study to design future smart home prototypes. Ain Shams Engineering Journal, 13, 101513. https://doi.org/10.1016/j.asej.2021.05.027 | |
| dc.relation.referencesen | 11. Teslyuk, V., Beregovska, K., & Denysyk, P. (2017). Decomposition of models of Smart-House - systems. In Proceedings of the XIIIth International Conference on Perspective Technologies and Methods in MEMS Design (pp. 22-24). Lviv, Ukraine. https://doi.org/10.1109/MEMSTECH.2017.7937524 | |
| dc.relation.referencesen | 12. Goessler, T., & Kaluarachchi, Y. (2023). Smart adaptive homes and their potential to improve space efficiency and personalisation. Buildings, 13(5), 1132. https://doi.org/10.3390/buildings13051132 | |
| dc.relation.referencesen | 13. Zolfaghari, S., Massa, S. M., & Riboni, D. (2023). Activity recognition in smart homes via feature-rich visual extraction of locomotion traces. Electronics, 12(9), 1969. https://doi.org/10.3390/electronics12091969 | |
| dc.relation.referencesen | 14. Najeh, H., Lohr, C., & Leduc, B. (2023). Convolutional neural network bootstrapped by dynamic segmentation and stigmergy-based encoding for real-time human activity recognition in smart homes. Sensors, 23, 1969. https://doi.org/10.3390/s23041969 | |
| dc.relation.referencesen | 15. Liu, J., Wang, M., & Wang, X. (2022). Research on General Model of Intelligence Level for Smart Home. In 7th International Conference on Computer and Communication Systems (ICCCS) (pp. 123-129). Wuhan, China. https://doi.org/10.1109/ICCCS55155.2022.9846791 | |
| dc.relation.referencesen | 16. Diallo, A., & Diallo, C. (2021). Human activity recognition in smart home using deep learning models. In International Conference on Computational Science and Computational Intelligence (pp. 1511-1515). Las Vegas, NV, USA. https://doi.org/10.1109/CSCI54926.2021.00294 | |
| dc.relation.referencesen | 17. Madhav, P. V., et al. (2023). Design and implementation of smart housing system for elderly persons. In International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (pp. 1-5). Chennai, India. https://doi.org/10.1109/RAEEUCCI57140.2023.10134421 | |
| dc.relation.referencesen | 18. Almarzooqi, H., Alzubaidi, I., Albahrani, A., Almansoori, A., & Shatnawi, M. (2019). Gas detection approaches in smart houses. In International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 726-731). Las Vegas, NV, USA. https://doi.org/10.1109/CSCI49370.2019.00138 | |
| dc.relation.referencesen | 19. Niu, H., Nguyen, D., Yonekawa, K., Kurokawa, M., Wada, S., & Yoshihara, K. (2020). Multi-source transfer learning for human activity recognition in smart homes. In IEEE International Conference on Smart Computing (pp. 274-277). Bologna, Italy. https://doi.org/10.1109/SMARTCOMP50058.2020.00063 | |
| dc.relation.referencesen | 20. Zhan, Y., & Haddadi, H. (2021). MoSen: Sensor network optimization in multiple-occupancy smart homes. In IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (pp. 384-388). Kassel, Germany. https://doi.org/10.1109/PerComWorkshops51409.2021.9430947 | |
| dc.relation.referencesen | 21. Nie, B. (2022). Pattern mining of smart home user behavior in the context of the Internet of Things: Based on sensor networks. In 3rd International Conference on Smart Electronics and Communication (pp. 398-401). Trichy, India. https://doi.org/10.1109/ICOSEC54921.2022.9952128 | |
| dc.relation.referencesen | 22. Cultice, T., Ionel, D., & Thapliyal, H. (2020). Smart home sensor anomaly detection using convolutional autoencoder neural network. In IEEE International Symposium on Smart Electronic Systems (iSES) (pp. 67). https://doi.org/10.1109/iSES50453.2020.00026 | |
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| dc.rights.holder | © Національний університет “Львівська політехніка”, 2024 | |
| dc.subject | мікроконтролер | |
| dc.subject | ESP8266 | |
| dc.subject | STM8 | |
| dc.subject | адаптивна система | |
| dc.subject | розумний дім | |
| dc.subject | microcontroller | |
| dc.subject | ESP8266 | |
| dc.subject | STM8 | |
| dc.subject | adaptive system | |
| dc.subject | smart home | |
| dc.title | Universal controller for the distributed management in the adaptive smart home systems | |
| dc.title.alternative | Універсальний контролер для розподіленого керування в адаптивних системах розумного дому | |
| dc.type | Article |
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