{"title":"FOG COMPUTING TECHNOLOGIES FOR PATIENT SENSOR NETWORKS – TRENDS, ISSUES AND FUTURE DIRECTIONS","authors":"Mantas Kazlauskas","doi":"10.3846/mla.2021.15174","DOIUrl":null,"url":null,"abstract":"Advances in sensors and internet of things promise broad opportunities in many areas and one of them is health care. There are many solutions to manage health care data based on cloud computing. However, high response latency, large volumes of data transferred and security are the main issues of such approach. Fog computing provides immediate response and ways to process large amounts of data using real time analytics which includes machine learning and AI. Fog computing has not yet fully matured and there are still many challenges when managing health care data. It was chosen to investigate the most relevant ehealth fog computing topics by analyzing review articles to explain the fog computing model and present the current trends – fog computing ehealth technology application environments, deployment cases, infrastructure technologies, data processing challenges, problems and future directions. 38 scientific review articles published in the last 5 years were selected for analysis, filtering the most significant works with Web of Science article search tool.","PeriodicalId":30324,"journal":{"name":"Mokslas Lietuvos Ateitis","volume":"54 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mokslas Lietuvos Ateitis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3846/mla.2021.15174","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Advances in sensors and internet of things promise broad opportunities in many areas and one of them is health care. There are many solutions to manage health care data based on cloud computing. However, high response latency, large volumes of data transferred and security are the main issues of such approach. Fog computing provides immediate response and ways to process large amounts of data using real time analytics which includes machine learning and AI. Fog computing has not yet fully matured and there are still many challenges when managing health care data. It was chosen to investigate the most relevant ehealth fog computing topics by analyzing review articles to explain the fog computing model and present the current trends – fog computing ehealth technology application environments, deployment cases, infrastructure technologies, data processing challenges, problems and future directions. 38 scientific review articles published in the last 5 years were selected for analysis, filtering the most significant works with Web of Science article search tool.
传感器和物联网的进步为许多领域带来了广阔的机会,其中之一就是医疗保健。有许多基于云计算的医疗保健数据管理解决方案。然而,高响应延迟、大数据传输量和安全性是这种方法的主要问题。雾计算提供即时响应和使用实时分析(包括机器学习和人工智能)处理大量数据的方法。雾计算尚未完全成熟,在管理医疗保健数据时仍存在许多挑战。通过分析评论文章来解释雾计算模型,并介绍当前趋势——雾计算电子卫生技术应用环境、部署案例、基础设施技术、数据处理挑战、问题和未来方向,研究最相关的电子卫生雾计算主题。选取近5年发表的38篇科学评论文章进行分析,利用Web of Science文章搜索工具筛选出最重要的作品。