The COVID-19 pandemic, which spread rapidly in late 2019, has revealed that the use of computing and communication technologies provides significant aid in preventing, controlling, and combating infectious diseases. With the ongoing research in next-generation networking (NGN), the use of secure and reliable communication and networking is of utmost importance when dealing with users' health records and other sensitive information. Through the adaptation of Artificial Intelligence (AI)-enabled NGN, the shape of healthcare systems can be altered to achieve smart and secure healthcare capable of coping with epidemics that may emerge at any given moment. In this article, we envision a cooperative and distributed healthcare framework that relies on state-of-the-art computing, communication, and intelligence capabilities, namely, Federated Learning (FL), mobile edge computing (MEC), and Blockchain, to enable epidemic (or suspicious infectious disease) discovery, remote monitoring, and fast health-authority response. The introduced framework can also enable secure medical data exchange at the edge and between different health entities. Such a technique, coupled with the low latency and high bandwidth functionality of 5G and beyond networks, would enable mass surveillance, monitoring and analysis to occur at the edge. Challenges, issues, and design guidelines are also discussed in this article with highlights on some trending solutions.
翻译:2019年后期迅速蔓延的COVID-19大流行表明,计算机和通信技术的使用在预防、控制和防治传染病方面提供了重要的帮助,随着下一代网络(NGN)的不断研究,使用安全可靠的通信和网络对于处理用户的健康记录和其他敏感信息至关重要,通过修改人工智能(AI)带动的NGN,保健系统的形状可以改变,以便实现智能和安全的保健,能够应付任何特定时刻可能出现的流行病。在本篇文章中,我们设想了一个合作和分布式保健框架,依靠最先进的计算、通信和情报能力,即联邦学习(FL)、移动边缘计算(MEC)和封锁链,以便能够发现流行病(或可疑传染病),进行远程监测和快速卫生当局反应。引入的框架还可以使边缘和不同卫生实体之间能够安全地交换医疗数据。这种技术,加上5G网络和以外网络的低纬度和高频带功能,将使得大规模监测、监测和分析能够在边缘出现一些解决办法。 挑战、问题和问题在设计中出现。