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.
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
https://doi.org/10.1109/STC-CSIT.2018.8526711
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
https://doi.org/10.1007/s12927-014-0006-4
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
https://doi.org/10.15587/1729-4061.2016.84143
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
https://doi.org/10.1109/AIACT.2019.8847748
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
https://doi.org/10.1109/ACCESS.2021.3128837
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
https://doi.org/10.3390/su15086902
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
https://doi.org/10.1109/STC-CSIT.2017.8098803
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
https://doi.org/10.1016/j.erss.2017.09.037
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
https://doi.org/10.1145/1978942.1979249
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
https://doi.org/10.1016/j.asej.2021.05.027
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
https://doi.org/10.1109/MEMSTECH.2017.7937524
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
https://doi.org/10.3390/buildings13051132
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
https://doi.org/10.3390/electronics12091969
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
https://doi.org/10.3390/s23041969
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
https://doi.org/10.1109/ICCCS55155.2022.9846791
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
https://doi.org/10.1109/CSCI54926.2021.00294
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
https://doi.org/10.1109/RAEEUCCI57140.2023.10134421
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
https://doi.org/10.1109/CSCI49370.2019.00138
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
https://doi.org/10.1109/SMARTCOMP50058.2020.00063
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
https://doi.org/10.1109/PerComWorkshops51409.2021.9430947
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
https://doi.org/10.1109/ICOSEC54921.2022.9952128
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
https://doi.org/10.1109/iSES50453.2020.00026
23. Khan, M., Saad, M. M., Tariq, M. A., Seo, J., & Kim, D. (2020). Human activity prediction-aware sensor cycling in smart home networks. In IEEE Globecom Workshops (pp. 1-6), Taipei, Taiwan. https://doi.org/10.1109/GCWkshps50303.2020.9367449
https://doi.org/10.1109/GCWkshps50303.2020.9367449
24. Shan, G., Lee, H., & Roh, B.-H. (2022). Indoor localization-based energy management for smart home. In IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC) (pp. 1-5), Melbourne, Australia. https://doi.org/10.1109/APPEEC53445.2022.10072129
https://doi.org/10.1109/APPEEC53445.2022.10072129
25. Wang, T., Cook, D. J., & Fischer, T. R. (2023). The indoor predictability of human mobility: Estimating mobility with smart home sensors. IEEE Transactions on Emerging Topics in Computing, 11(1), 182-193. https://doi.org/10.1109/TETC.2022.3188939
https://doi.org/10.1109/TETC.2022.3188939
26. Rokonuzzaman, M., Akash, M. I., Khatun Mishu, M., Tan, W.-S., Hannan, M. A., & Amin, N. (2022). IoT-based distribution and control system for smart home applications. In IEEE 12th Symposium on Computer Applications & Industrial Electronics (pp. 95-98), Penang, Malaysia. https://doi.org/10.1109/ISCAIE54458.2022.9794497
https://doi.org/10.1109/ISCAIE54458.2022.9794497
27. Tayef, S. H., Rahman, M. M., & Sakib, M. A. B. (2021). Design and implementation of IoT based smart home automation system. In 24th International Conference on Computer and Information Technology (ICCIT) (pp. 1-5), Dhaka, Bangladesh. https://doi.org/10.1109/ICCIT54785.2021.9689809
https://doi.org/10.1109/ICCIT54785.2021.9689809
28. Sharma, S., Sharma, A., Goel, T., Deoli, R., & Mohan, S. (2020). Smart home gardening management system: A cloud-based Internet-of-Things (IoT) application in VANET. In 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-5), Kharagpur, India. https://doi.org/10.1109/ICCCNT49239.2020.9225573
https://doi.org/10.1109/ICCCNT49239.2020.9225573
29. Kang, B., Kim, S., Choi, M.-I., Cho, K., Jang, S., & Park, S. (2016). Analysis of types and importance of sensors in smart home services. In IEEE 18th International Conference on High Performance Computing and Communications (pp. 1388-1389), Sydney, NSW, Australia. https://doi.org/10.1109/HPCC-SmartCity-DSS.2016.0196
https://doi.org/10.1109/HPCC-SmartCity-DSS.2016.0196
30. Yoon, Y., Lee, J., Lee, J., Kim, B., & Jembre, Y. Z. (2020). Adaptive sensor data transmission scheduling scheme for smart home networks. In IEEE 92nd Vehicular Technology Conference (pp. 1-3), Victoria, BC, Canada. https://doi.org/10.1109/VTC2020-Fall49728.2020.9348564
https://doi.org/10.1109/VTC2020-Fall49728.2020.9348564
31. Romadhon, A. S., & Widyaningrum, V. T. (2022). Application of sensors in Arduino as a control in smart home. In IEEE 8th Information Technology International Seminar (ITIS) (pp. 130-133), Surabaya, Indonesia. https://doi.org/10.1109/ITIS57155.2022.10010217
https://doi.org/10.1109/ITIS57155.2022.10010217
32. Macheso, P., Manda, T. D., Chisale, S., Dzupire, N., Mlatho, J., & Mukanyiligira, D. (2021). Design of ESP8266 smart home using MQTT and Node-RED. In International Conference on Artificial Intelligence and Smart Systems (pp. 502-505), Coimbatore, India. https://doi.org/10.1109/ICAIS50930.2021.9396027
https://doi.org/10.1109/ICAIS50930.2021.9396027
33. Zhang, Y., Meng, Z., Shen, R., Hou, L., & Liu, J. (2021). Electrical design and application of smart home system based on distributed control. In IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (pp. 723-727), Chongqing, China. https://doi.org/10.1109/IAEAC50856.2021.9391001
https://doi.org/10.1109/IAEAC50856.2021.9391001
34. Sarhan, Q. I. (2020). Arduino based smart home warning system. In IEEE 6th International Conference on Control Science and Systems Engineering (pp. 201-206), Beijing, China. https://doi.org/10.1109/ICCSSE50399.2020.917193
https://doi.org/10.1109/ICCSSE50399.2020.9171939
35. Narkthong, N., Duan, S., Ren, S., & Xu, X. (2024) MicroVSA: An Ultra-Lightweight Vector Symbolic Architecture-based Classifier Library for Always-On Inference on Tiny Microcontrollers. In the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2, (pp. 730-745), Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3620665.3640374
https://doi.org/10.1145/3620665.3640374