A fog-computing architecture for preventive healthcare and assisted living in smart ambients
2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI)(2017)
摘要
In the last years, the emergence of pervasive connected devices and the development of the cloud computing paradigms brought a revolution in health-care and industrial applications. Cloud and Internet of Things (IoT) exploit large scale service providers to vastly reduce costs and gather Big Data. However, cloud-based services still face various issues related to: high bandwidth requirements, unpredictable delays, and security and safety concerns. These issues are critical to health-care and Active and Assisted Living (AAL) where a correct and timely reaction can result in saving a life or drastically reducing a disability (e.g. after a stroke). In this scenario, we present a flexible multi-level architecture using the fog approach, a computing paradigm in which heterogeneous devices at the edge of the network collect data, compute a task with minimal latency, and produce physical actions meaningful for the user, leveraging upon context and location awareness. In this paper, we envision also an edge node built upon Field-Programmable Gate Array (FPGA) technology. The hardware of a FPGA node can be reconfigured to produce maximum performance in tasks, to guarantee a minimal delay, or the capacity to scale on the number of devices connected, with a minimal power consumption. We present two case studies for assistive smart ambients and health applications designed on our Fog architecture.
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关键词
IoT,Big Data,unpredictable delays,safety concerns,flexible multilevel architecture,heterogeneous devices,location awareness,edge node,Field-Programmable Gate Array technology,FPGA node,minimal delay,minimal power consumption,assistive smart ambients,fog-computing architecture,preventive healthcare,pervasive connected devices,industrial applications,service providers,cloud computing,Internet of Things,cost reduction,active and assisted living,AAL,network collect data collection
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