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Fuzzy-based Fog Computing for Real-Time Data Transmission in Healthcare Internet-of-Things

机译:基于模糊的雾计算,用于医疗物联网中的实时数据传输

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The increase in the growth of healthcare IoT devices leads to a large volume and variety of healthcare data. The large healthcare data generates high data traffic that will cause network bottleneck and burdens the cloud with an extra amount of workload. The main requirement of healthcare IoT is to have a minimum network and service latency along with conserve network bandwidth. Both the volume of data as well as factors related to internet connectivity may lead to high network latency in analyzing and acting upon device data. Therefore, due to high network latency, high service latency and large data transmission from IoT devices, the cloud is unable to send the patient health data in real-time mode. Fog computing is a new approach to address this requirement of cloud and healthcare IoT devices. A tentative novel solution for the above-mentioned problem using fuzzy-based fog computing architecture that integrates healthcare IoT devices with the cloud and uses fog services with Fuzzy Data Packet Allocation (FDPA) algorithm. Using virtualization and fuzzy inference system (FIS) in fog computing, the proposed algorithm is expected to transfer the patient health data in real-time mode to agencies and doctors.
机译:医疗保健物联网设备的增长增加导致医疗保健数据量大且种类繁多。庞大的医疗数据会产生大量数据流量,这将导致网络瓶颈,并给云造成额外的工作量。医疗物联网的主要要求是具有最小的网络和服务延迟以及节省的网络带宽。数据量以及与Internet连接性相关的因素都可能导致在分析和处理设备数据时出现较高的网络延迟。因此,由于高网络延迟,高服务延迟和来自IoT设备的大量数据传输,云无法以实时模式发送患者健康数据。雾计算是一种新方法,可满足云和医疗物联网设备的这一需求。使用基于模糊的雾计算体系结构解决上述问题的尝试性新解决方案,该体系结构将医疗保健物联网设备与云集成在一起,并使用带有模糊数据包分配(FDPA)算法的雾服务。在雾计算中使用虚拟化和模糊推理系统(FIS),该算法有望以实时模式将患者健康数据传输给代理商和医生。

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