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Edge computing and its role in Industrial Internet: Methodologies, applications, and future directions

机译:边缘计算及其在工业互联网中的作用:方法,应用和未来方向

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摘要

Proliferation of Industrial Internet has dramatically changed the way we live and work. It brings convenience to our society and sometimes requires real-time processing of dramatic data at the same time. However, traditional paradigm of computing on the center cloud can't always meet such requirement, for the non-negligible time delay of data transmission and communication. Edge computing is a novel computing paradigm proposed to resolve such a problem. As a promising technology, it extends computing from cloud center to the edge of network. Edge computing has the advantage of low latency to achieve a shorter response time, as well as potential to address the concerns of energy consuming, bandwidth burden and security issue. In this paper, we give a survey about edge computing from the aspect of methodologies, application scenarios and its role in Industrial Internet. Some open issues of edge computing are also introduced in this paper. At the end of the manuscript, a discussion about future direction is proposed. The shallow network algorithms such as broad learning system (BLS), which have achieved great improvement in computing efficiency, show an optimistic outlook in this area. We propose our conceive about future applications when shallow network methods like BLS are applied in edge computing and hope the paper will inspire research in relative directions. (C) 2020 Elsevier Inc. All rights reserved.
机译:工业互联网的激增极大地改变了我们的生活和工作方式。它给我们的社会带来了便利,有时还需要实时处理戏剧性的数据。然而,由于数据传输和通信的时间延迟不可忽略,传统的中心云计算模式无法始终满足这种要求。边缘计算是为解决这一问题而提出的一种新的计算范式。作为一项很有前途的技术,它将计算从云中心扩展到了网络边缘。边缘计算具有低延迟的优势,可以实现更短的响应时间,并且有可能解决能耗、带宽负担和安全问题。本文从边缘计算的方法、应用场景及其在工业互联网中的作用等方面对边缘计算进行了综述。本文还介绍了边缘计算的一些开放性问题。在手稿的最后,对未来的发展方向进行了讨论。以广义学习系统(BLS)为代表的浅层网络算法在计算效率上有了很大的提高,在这方面表现出了乐观的前景。我们对BLS等浅层网络方法在边缘计算中的应用提出了设想,希望本文能对相关领域的研究有所启发。(C) 2020爱思唯尔公司版权所有。

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