A Survey on the Edge Computing for the Internet of Things

Wei Yu; Fan Liang; Xiaofei He; William Grant Hatcher; Chao Lu; Jie Lin; Xinyu Yang

doi:10.1109/ACCESS.2017.2778504

IEEE Access (Jan 2018)

A Survey on the Edge Computing for the Internet of Things

Wei Yu,
Fan Liang,
Xiaofei He,
William Grant Hatcher,
Chao Lu,
Jie Lin,
Xinyu Yang

Affiliations

Wei Yu: ORCiD; Department of Computer and Information Sciences, Towson University, Towson, MD, USA
Fan Liang: Department of Computer and Information Sciences, Towson University, Towson, MD, USA
Xiaofei He: Department of Computer and Information Sciences, Towson University, Towson, MD, USA
William Grant Hatcher: Department of Computer and Information Sciences, Towson University, Towson, MD, USA
Chao Lu: Department of Computer and Information Sciences, Towson University, Towson, MD, USA
Jie Lin: School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China
Xinyu Yang: School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China

DOI: https://doi.org/10.1109/ACCESS.2017.2778504
Journal volume & issue: Vol. 6
pp. 6900 – 6919

Abstract

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The Internet of Things (IoT) now permeates our daily lives, providing important measurement and collection tools to inform our every decision. Millions of sensors and devices are continuously producing data and exchanging important messages via complex networks supporting machine-to-machine communications and monitoring and controlling critical smart-world infrastructures. As a strategy to mitigate the escalation in resource congestion, edge computing has emerged as a new paradigm to solve IoT and localized computing needs. Compared with the well-known cloud computing, edge computing will migrate data computation or storage to the network “edge”, near the end users. Thus, a number of computation nodes distributed across the network can offload the computational stress away from the centralized data center, and can significantly reduce the latency in message exchange. In addition, the distributed structure can balance network traffic and avoid the traffic peaks in IoT networks, reducing the transmission latency between edge/cloudlet servers and end users, as well as reducing response times for real-time IoT applications in comparison with traditional cloud services. Furthermore, by transferring computation and communication overhead from nodes with limited battery supply to nodes with significant power resources, the system can extend the lifetime of the individual nodes. In this paper, we conduct a comprehensive survey, analyzing how edge computing improves the performance of IoT networks. We categorize edge computing into different groups based on architecture, and study their performance by comparing network latency, bandwidth occupation, energy consumption, and overhead. In addition, we consider security issues in edge computing, evaluating the availability, integrity, and the confidentiality of security strategies of each group, and propose a framework for security evaluation of IoT networks with edge computing. Finally, we compare the performance of various IoT applications (smart city, smart grid, smart transportation, and so on) in edge computing and traditional cloud computing architectures.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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