基于FoG和IoT网络的医疗数据管理系统研究与研究方向

Tina Victoria A, M. Kowsigan
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引用次数: 0

摘要

由于医疗传感设备数量的增加,研究人员对其产生了浓厚的兴趣,因为它具有新的复杂和更广泛的应用场景。在“支持物联网(IoT)的无线传感器网络(WSN)”中展示了多个连接设备,该网络通过新的研究限制和创新解决方案,具有与临床健康约束相关的多个传感支持系统范围。WSN由传感器节点组成,通过利用传感设备测量步行步数、跌倒检测、体温、血压、心跳和血糖水平,与医疗保健部门协调监测老年家庭成员或患者。面向fog辅助技术的数据聚合使用传感设备和可穿戴设备之间的“点对点通信”来协助健康。在这种情况下,集中式服务器的安全数据收集和传输更加复杂,以防止各种针对非法访问数据的攻击。传统的解决方案受到能源、通信和存储开销的困扰。本文的主要目的是对物联网wsn中适用于“fog辅助医疗数据”管理系统的不同算法进行分类,以帮助该研究领域的初学者。此外,本综述着重于用于实现的各种工具和为验证而分析的性能度量。最后,给出了未来需要关注的研究空白和挑战,为未来研究者进入数据管理模型提供思路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Study and Research Direction towards Healthcare Data Management System in FoG and IoT Networks
Due to the rise in the count of sensing devices for healthcare, the researchers are showing high interest due to the new complicated and broader range of application scenarios. Several numbers of connected devices are presented in the “Internet of Things (IoT)-enabled Wireless Sensor Network (WSN),” which has several ranges of sensing support systems correlated with clinical health constraints through new research limitations and innovative solutions. WSN consists of sensor nodes, which are coordinated with healthcare to monitor aging family members or patients through utilizing sensing devices to measure walk step counting, fall detection, temperature, blood pressure, heartbeat, and glucose level. Data aggregation towards FoG-assisted technologies uses “peer-to-peer communication” among sensing devices and wearables to assist health. In these cases, secure data gathering and transmission for centralized servers are more complicated for protecting against various attacks towards illicit accessing of data. Conventional solutions are suffered from energy, communication, and storage overheads. The main intention of this paper is to categorize different algorithms suited for “FoG-assisted healthcare data” management systems in IoT-enabled WSNs to assist beginners in this research area. Moreover, this review focuses on diverse tools used for implementation and performance metrics analyzed for validation. Finally, the research gaps and challenges to be focused on in the future are also given, thus giving ideas for future researchers to get into the data management model.
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