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Model and Structure of IoT Network for Alzheimer’s Disease Diagnostics

https://doi.org/10.35596/1729-7648-2024-22-4-105-113

Abstract

The article presents the structure and model of an Internet of Things network that can be used for remote rapid detection of Alzheimer’s disease. A local server model of the Internet of Things network has been created for personalized medical care on the client side. The model corresponds to the characteristics of the Internet of Things network: interconnection between devices, real-time communication, data processing and analysis, the use of various protocols for data transfer and exchange. When building the model, the Flask framework was used to create an application instance with a trigger condition for sending data from a smartphone to a local server via an HTTP request. The local server receives the HTTP request sent by the smartphone and processes the data. The result of the procedure is transmitted through the MQTT protocol to the MQTT client that has been subscribed to certain topics, i.e., the smartphone. Taking into account the selected structure and configuration of the Internet of Things network device, a complete model of this network was built, which can be applied to various applications. The functions and performance of the model are verified through experiments.

About the Authors

U. A. Vishniakou
Belarusian State University of Informatics and Radioelectronics (BSUIR)
Belarus

Vishniakou Uladzimir Anatol’evich, Dr. of Sci. (Tech.), Professor at the Department of Infocommunication Technologies,

6, P. Brovki St., Minsk, 220013

Phone: +375 44 486-71-82.



Yu Chuyue
Belarusian State University of Informatics and Radioelectronics (BSUIR)
Belarus

Chuyue Yu, Postgraduate at the Department of Infocommunication Technologies, 

Minsk.



References

1. Dong L. (2023) Application of Internet of Things Technology in Hospital Information Construction. Shihezi Science and Technology. (3), 77–78.

2. Vishniakou U. A. (2023) Specialised IoT Systems: Models, Structures, Algorithms, Hardware, Software Tools. Minsk: Belarusian State University of Informatics and Radioelectronics.

3. Vishniakou U. A., Chuyue Yu. (2023) Using Machine Learning for Recognition of Alzheimer’s Disease Based on Transcription Information. Doklady BGUIR. 21 (6), 106–112. http://dx.doi.org/10.35596/1729-7648-202321-6-106-112.

4. Jiafa C., Yujing H. (2022) Application of Flask Framework in Data Visualization. Fujian Computer. 38 (12), 44–48.

5. Grinberg M. (2018) Flask Web Development: Developing Web Applications with Python. O’Reilly Media, Inc.


Review

For citations:


Vishniakou U.A., Chuyue Yu. Model and Structure of IoT Network for Alzheimer’s Disease Diagnostics. Doklady BGUIR. 2024;22(4):105-113. https://doi.org/10.35596/1729-7648-2024-22-4-105-113

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This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 1729-7648 (Print)
ISSN 2708-0382 (Online)