支持物联网的医疗保健解决方案中的云雾互操作性

Md. Redowan Mahmud, F. Koch, R. Buyya
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引用次数: 160

摘要

在医疗保健解决方案中利用物联网(IoT)的问题涉及延迟敏感性、不均匀的数据负载、不同的用户期望和应用程序的异构性等问题。目前的探索将云计算作为创建物联网解决方案的基石。尽管如此,这种环境在数据源的多跳距离、地理集中式体系结构、经济方面等方面存在限制。为了解决这些限制,出现了大量应用雾计算的解决方案,将雾计算作为一种使计算资源更接近数据源的方法。这种方法是由强大的边缘计算以更低的成本不断增长的可用性和该领域的商业发展所推动的。尽管如此,Cloud-Fog互操作性和集成的实现意味着应用程序和服务的复杂协调,以及对智能服务编排的需求,以便解决方案能够在不影响稳定性、服务质量和安全性的情况下充分利用分布式资源。在本文中,我们介绍了一种基于雾的物联网医疗解决方案结构,并探索了在传统基于云的结构基础上扩展的可互操作医疗解决方案中云雾服务的集成。通过使用iFogSim模拟器对这些场景进行了仿真评估,并分析了与分布式计算、减少延迟、优化数据通信和功耗相关的结果。实验结果表明,该方法在实例成本、网络延迟和能源使用方面都有改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cloud-Fog Interoperability in IoT-enabled Healthcare Solutions
The issue of utilizing Internet of Things (IoT) in Healthcare solutions relates to the problems of latency sensitivity, uneven data load, diverse user expectations and heterogeneity of the applications. Current explorations consider Cloud Computing as the base stone to create IoT-Enable solution. Nonetheless, this environment entails limitations in terms of multi-hop distance from the data source, geographical centralized architecture, economical aspects, etc. To address these limitations, there is a surge of solutions that apply Fog Computing as an approach to bring computing resources closer to the data sources. This approach is being fomented by the growing availability of powerful edge computing at lower cost and commercial developments in the area. Nonetheless, the implementation of Cloud-Fog interoperability and integration implies in complex coordination of applications and services and the demand for intelligent service orchestrations so that solutions can make the best use of distributed resources without compromising stability, quality of services, and security. In this paper, we introduce a Fog-based IoT-Healthcare solution structure and explore the integration of Cloud-Fog services in interoperable Healthcare solutions extended upon the traditional Cloud-based structure. The scenarios are evaluated through simulations using the iFogSim simulator and the results analyzed in relation to distributed computing, reduction of latency, optimization of data communication, and power consumption. The experimental results point towards improvement in instance cost, network delay and energy usage.
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