云上的医疗传感器数据管理

Pelagia Tsiachri Renta, Stelios Sotiriadis, E. Petrakis
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引用次数: 16

摘要

通过使用云计算和物联网等新技术支持医疗保健程序,可以显著提高医疗服务质量。远程和实时监测患者健康的需求越来越重要,特别是对慢性病患者和老年人。在这项工作中,我们专注于管理由蓝牙低功耗设备产生的存储在云上的医疗保健相关数据。我们提出了一个基于云的物联网管理系统,可以实时收集重要的用户数据(例如心脏脉搏率和血氧饱和度)。我们的解决方案能够快速高效地收集和处理传感器数据,同时医务人员等用户可以订阅患者数据并获得通知。该系统是基于微服务设计的,包括为卫生保健提供者和患者提供的通知服务,最大限度地减少对紧急情况反应迟缓的风险。警报是根据预定义的规则和患者特定的反应计划产生的。我们提出了一项实验研究,我们基于真实世界的传感器评估我们的系统,同时我们生成一个模拟数千用户的合成数据集。结果很好,因为即使在与接收服务请求的web服务器的限制绑定的繁重负载下,系统也接近实时响应。当系统部署在2GB RAM、1个VCPU和20GB磁盘的虚拟机上时,最重的工作负载模拟了2000个用户请求(其中80个并发执行)在不到13秒的时间内完成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Healthcare Sensor Data Management on the Cloud
The quality of medical services can be significantly improved by supporting health care procedures with new technologies such as Cloud computing and Internet of Things (IoTs). The need to monitor patient's health remotely and in real time becomes more and more a vital requirement, especially for chronic patients and elderly. In this work, we focus on the management of health care related data stored on the Cloud produced by Bluetooth low energy devices. We present a Cloud based IoT Management System that collects vital user data (e.g. cardiac pulse rate and blood oxygen saturation) on real time. Our solution enables sensor data collection and processing fast and efficient, while users such as medical personnel can subscribe to patient's data and get notifications. The system is designed based on microservices and includes a notification service for both health care providers and patients minimizing the risk of late response to emergency conditions. Alerts are produced according to predefined rules and on patient specific reaction plans. We present an experimental study where we evaluate our system based on real world sensors, while we generate a synthetic dataset for simulating thousands of users. The results are prosperous, as the system responds close to real time even under heavy loads binding to the limits of the web server that receives the service request. The heaviest workload simulates 2000 user requests (while 80 are executed concurrently) is completed in less than 13 seconds when the system deployed in a virtual machine of 2GB RAM, 1 VCPU and 20GB Disk.
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