物联网在卫生领域的应用:实现能源消耗最小化

IF 7.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Mohammed Moutaib;Tarik Ahajjam;Mohammed Fattah;Yousef Farhaoui;Badraddine Aghoutane;Moulhime El Bekkali
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引用次数: 1

摘要

物联网(IoT)目前反映在连接对象数量的增加上,即具有自身身份、计算和通信能力的设备。物联网被公认为未来技术最关键的领域之一,受到全世界的关注。它适用于许多已经取得成功的领域,例如医疗保健,使用节点和轻量级传感器监测患者。然而,物联网在医疗领域的强大功能是基于数据的自主通信、分析、处理和管理,而无需任何人工干预,这带来了许多困难,如能耗。然而,这些问题大大减缓了这项技术的开发和快速部署。来自连接对象的能量浪费的主要原因包括当两个或多个节点同时发送数据时发生的冲突,以及当发生冲突或由于信道衰落导致数据未正确接收时发生的数据重传的主要原因。节点之间的距离是影响能耗的因素之一。在本文中,我们提出了节点之间的直接通信以避免域冲突,这将有助于减少数据重传。结果表明,与集中式和现有工作相比,分布式可以确保系统在一般条件下的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Internet of Things in the Health Sector: Toward Minimizing Energy Consumption
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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来源期刊
Big Data Mining and Analytics
Big Data Mining and Analytics Computer Science-Computer Science Applications
CiteScore
20.90
自引率
2.20%
发文量
84
期刊介绍: Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.
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