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物联网医疗中用于多媒体传感的智能传感器架构

Smart Sensor Architectures for Multimedia Sensing in IoMT.

作者信息

Silvestre-Blanes Javier, Sempere-Payá Víctor, Albero-Albero Teresa

机构信息

ITI and Universitat Politècnica de València (UPV), DISCA, EPSA, 03801 Alcoy, Spain.

ITI and Universitat Politècnica de València (UPV), DCOM, ETSIT, 46022 Valencia, Spain.

出版信息

Sensors (Basel). 2020 Mar 4;20(5):1400. doi: 10.3390/s20051400.

Abstract

Today, a wide range of developments and paradigms require the use of embedded systems characterized by restrictions on their computing capacity, consumption, cost, and network connection. The evolution of the Internet of Things (IoT) towards Industrial IoT (IIoT) or the Internet of Multimedia Things (IoMT), its impact within the 4.0 industry, the evolution of cloud computing towards edge or fog computing, also called near-sensor computing, or the increase in the use of embedded vision, are current examples of this trend. One of the most common methods of reducing energy consumption is the use of processor frequency scaling, based on a particular policy. The algorithms to define this policy are intended to obtain good responses to the workloads that occur in smarthphones. There has been no study that allows a correct definition of these algorithms for workloads such as those expected in the above scenarios. This paper presents a method to determine the operating parameters of the dynamic governor algorithm called , which offers significant improvements in power consumption, without reducing the performance of the application. These improvements depend on the load that the system has to support, so the results are evaluated against three different loads, from higher to lower, showing improvements ranging from 62% to 26%.

摘要

如今,广泛的发展和范式需要使用嵌入式系统,这些系统的特点是在计算能力、功耗、成本和网络连接方面受到限制。物联网(IoT)向工业物联网(IIoT)或多媒体物联网(IoMT)的演进、其在4.0行业中的影响、云计算向边缘或雾计算(也称为近传感器计算)的演进,或者嵌入式视觉使用的增加,都是这一趋势的当前例子。降低能耗最常用的方法之一是基于特定策略使用处理器频率缩放。定义此策略的算法旨在对智能手机中出现的工作负载获得良好响应。对于上述场景中预期的工作负载类型,尚无研究能正确定义这些算法。本文提出了一种确定名为 的动态调速器算法操作参数的方法,该方法在不降低应用性能的情况下,显著降低了功耗。这些改进取决于系统必须支持的负载,因此针对三种不同负载(从高到低)对结果进行了评估,显示出62%至26%的改进幅度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3167/7085541/e60782704396/sensors-20-01400-g001.jpg

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