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Open Access

Application of Internet of Things in the Health Sector: Toward Minimizing Energy Consumption

IMAGE Laboratory, Moulay Ismail University, Meknes 50000, Morocco
Department of Computer Science, Faculty of Sciences and Techniques, Moulay Ismail University, Errachidia 52000, Morocco
IA Laboratory, Faculty of Science, Moulay Ismail University, Meknes 50000, Morocco
Sidi Mohamed Ben Abdellah University, Fes 30000, Morocco
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Abstract

The Internet of Things (IoT) is currently reflected in the increase in the number of connected objects, that is, devices with their own identity and computing and communication capacities. IoT is recognized as one of the most critical areas for future technologies, gaining worldwide attention. It applies to many areas, where it has achieved success, such as healthcare, where a patient is monitored using nodes and lightweight sensors. However, the powerful functions of IoT in the medical field are based on communication, analysis, processing, and management of data autonomously without any manual intervention, which presents many difficulties, such as energy consumption. However, these issues significantly slow down the development and rapid deployment of this technology. The main causes of wasted energy from connected objects include collisions that occur when two or more nodes send data simultaneously and the leading cause of data retransmission that occurs when a collision occurs or when data are not received correctly due to channel fading. The distance between nodes is one of the factors influencing energy consumption. In this article, we have proposed direct communication between nodes to avoid collision domains, which will help reduce data retransmission. The results show that the distribution can ensure the performance of the system under general conditions compared to the centralization and to the existing works.

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Big Data Mining and Analytics
Pages 302-308
Cite this article:
Moutaib M, Ahajjam T, Fattah M, et al. Application of Internet of Things in the Health Sector: Toward Minimizing Energy Consumption. Big Data Mining and Analytics, 2022, 5(4): 302-308. https://doi.org/10.26599/BDMA.2021.9020031

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Received: 30 November 2021
Accepted: 28 December 2021
Published: 18 July 2022
© The author(s) 2022.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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