Edge Computing: Applications, State-of-the-Art and Challenges

Shufen Wang

doi:doi:10.11648/j.net.20190701.12

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Edge Computing: Applications, State-of-the-Art and Challenges

Shufen Wang

Published in Advances in Networks (Volume 7, Issue 1)

Received: 10 October 2019 Accepted: 31 October 2019 Published: 15 November 2019

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Abstract

The Internet of Things (IoT) is now infiltrating into our daily lives, providing important measurement and collection tools to inform us of every decision. Millions of sensors and devices continue to generate data and exchange important information through complex networks that support machine-to-machine communication and monitor and control critical smart world infrastructure. As a strategy to alleviate resource congestion escalation, edge computing has become a new paradigm for addressing the needs of the Internet of Things and localization computing. Compared to well-known cloud computing, edge computing migrates data calculations or storage to the edge of the network near the end-user. Thus, multiple compute nodes distributed across the network can offload computational pressure from a centralized data center and can significantly reduce latency in message exchanges. Besides, the distributed architecture balances network traffic and avoids spikes in traffic in the IoT network, reduces latency between edge/cloud servers and end-users, and reduces response time for real-time IoT applications compared to traditional cloud services. In this article, we conducted a comprehensive survey to analyze how edge computing can improve the performance of IoT networks. We classify edge calculations into different groups based on the architecture and study their performance by comparing network latency, bandwidth usage, power consumption, and overhead. Through the systematic introduction of the concept of edge computing, typical application scenarios, research status, and key technologies, it is considered that the development of edge computing is still in the initial stage. There are still many problems in practical applications that need to be solved, including optimizing edge computing performance, security, interoperability, and intelligent edge operations management services.

Published in	Advances in Networks (Volume 7, Issue 1)
DOI	10.11648/j.net.20190701.12
Page(s)	8-15
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Edge Computing, Security, Interoperability

References

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Shufen Wang. (2019). Edge Computing: Applications, State-of-the-Art and Challenges. Advances in Networks, 7(1), 8-15. https://doi.org/10.11648/j.net.20190701.12

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ACS Style

Shufen Wang. Edge Computing: Applications, State-of-the-Art and Challenges. Adv. Netw. 2019, 7(1), 8-15. doi: 10.11648/j.net.20190701.12

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AMA Style

Shufen Wang. Edge Computing: Applications, State-of-the-Art and Challenges. Adv Netw. 2019;7(1):8-15. doi: 10.11648/j.net.20190701.12

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@article{10.11648/j.net.20190701.12,
  author = {Shufen Wang},
  title = {Edge Computing: Applications, State-of-the-Art and Challenges},
  journal = {Advances in Networks},
  volume = {7},
  number = {1},
  pages = {8-15},
  doi = {10.11648/j.net.20190701.12},
  url = {https://doi.org/10.11648/j.net.20190701.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.net.20190701.12},
  abstract = {The Internet of Things (IoT) is now infiltrating into our daily lives, providing important measurement and collection tools to inform us of every decision. Millions of sensors and devices continue to generate data and exchange important information through complex networks that support machine-to-machine communication and monitor and control critical smart world infrastructure. As a strategy to alleviate resource congestion escalation, edge computing has become a new paradigm for addressing the needs of the Internet of Things and localization computing. Compared to well-known cloud computing, edge computing migrates data calculations or storage to the edge of the network near the end-user. Thus, multiple compute nodes distributed across the network can offload computational pressure from a centralized data center and can significantly reduce latency in message exchanges. Besides, the distributed architecture balances network traffic and avoids spikes in traffic in the IoT network, reduces latency between edge/cloud servers and end-users, and reduces response time for real-time IoT applications compared to traditional cloud services. In this article, we conducted a comprehensive survey to analyze how edge computing can improve the performance of IoT networks. We classify edge calculations into different groups based on the architecture and study their performance by comparing network latency, bandwidth usage, power consumption, and overhead. Through the systematic introduction of the concept of edge computing, typical application scenarios, research status, and key technologies, it is considered that the development of edge computing is still in the initial stage. There are still many problems in practical applications that need to be solved, including optimizing edge computing performance, security, interoperability, and intelligent edge operations management services.},
 year = {2019}
}

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TY  - JOUR
T1  - Edge Computing: Applications, State-of-the-Art and Challenges
AU  - Shufen Wang
Y1  - 2019/11/15
PY  - 2019
N1  - https://doi.org/10.11648/j.net.20190701.12
DO  - 10.11648/j.net.20190701.12
T2  - Advances in Networks
JF  - Advances in Networks
JO  - Advances in Networks
SP  - 8
EP  - 15
PB  - Science Publishing Group
SN  - 2326-9782
UR  - https://doi.org/10.11648/j.net.20190701.12
AB  - The Internet of Things (IoT) is now infiltrating into our daily lives, providing important measurement and collection tools to inform us of every decision. Millions of sensors and devices continue to generate data and exchange important information through complex networks that support machine-to-machine communication and monitor and control critical smart world infrastructure. As a strategy to alleviate resource congestion escalation, edge computing has become a new paradigm for addressing the needs of the Internet of Things and localization computing. Compared to well-known cloud computing, edge computing migrates data calculations or storage to the edge of the network near the end-user. Thus, multiple compute nodes distributed across the network can offload computational pressure from a centralized data center and can significantly reduce latency in message exchanges. Besides, the distributed architecture balances network traffic and avoids spikes in traffic in the IoT network, reduces latency between edge/cloud servers and end-users, and reduces response time for real-time IoT applications compared to traditional cloud services. In this article, we conducted a comprehensive survey to analyze how edge computing can improve the performance of IoT networks. We classify edge calculations into different groups based on the architecture and study their performance by comparing network latency, bandwidth usage, power consumption, and overhead. Through the systematic introduction of the concept of edge computing, typical application scenarios, research status, and key technologies, it is considered that the development of edge computing is still in the initial stage. There are still many problems in practical applications that need to be solved, including optimizing edge computing performance, security, interoperability, and intelligent edge operations management services.
VL  - 7
IS  - 1
ER  -

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Author Information

Shufen Wang

College of Information Engineering, Harbin Institute of Petroleum, Harbin, China

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Plain Text BibTeX RIS

APA Style

Shufen Wang. (2019). Edge Computing: Applications, State-of-the-Art and Challenges. Advances in Networks, 7(1), 8-15. https://doi.org/10.11648/j.net.20190701.12

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ACS Style

Shufen Wang. Edge Computing: Applications, State-of-the-Art and Challenges. Adv. Netw. 2019, 7(1), 8-15. doi: 10.11648/j.net.20190701.12

Copy | Download

AMA Style

Shufen Wang. Edge Computing: Applications, State-of-the-Art and Challenges. Adv Netw. 2019;7(1):8-15. doi: 10.11648/j.net.20190701.12

Copy | Download

@article{10.11648/j.net.20190701.12,
  author = {Shufen Wang},
  title = {Edge Computing: Applications, State-of-the-Art and Challenges},
  journal = {Advances in Networks},
  volume = {7},
  number = {1},
  pages = {8-15},
  doi = {10.11648/j.net.20190701.12},
  url = {https://doi.org/10.11648/j.net.20190701.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.net.20190701.12},
  abstract = {The Internet of Things (IoT) is now infiltrating into our daily lives, providing important measurement and collection tools to inform us of every decision. Millions of sensors and devices continue to generate data and exchange important information through complex networks that support machine-to-machine communication and monitor and control critical smart world infrastructure. As a strategy to alleviate resource congestion escalation, edge computing has become a new paradigm for addressing the needs of the Internet of Things and localization computing. Compared to well-known cloud computing, edge computing migrates data calculations or storage to the edge of the network near the end-user. Thus, multiple compute nodes distributed across the network can offload computational pressure from a centralized data center and can significantly reduce latency in message exchanges. Besides, the distributed architecture balances network traffic and avoids spikes in traffic in the IoT network, reduces latency between edge/cloud servers and end-users, and reduces response time for real-time IoT applications compared to traditional cloud services. In this article, we conducted a comprehensive survey to analyze how edge computing can improve the performance of IoT networks. We classify edge calculations into different groups based on the architecture and study their performance by comparing network latency, bandwidth usage, power consumption, and overhead. Through the systematic introduction of the concept of edge computing, typical application scenarios, research status, and key technologies, it is considered that the development of edge computing is still in the initial stage. There are still many problems in practical applications that need to be solved, including optimizing edge computing performance, security, interoperability, and intelligent edge operations management services.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Edge Computing: Applications, State-of-the-Art and Challenges
AU  - Shufen Wang
Y1  - 2019/11/15
PY  - 2019
N1  - https://doi.org/10.11648/j.net.20190701.12
DO  - 10.11648/j.net.20190701.12
T2  - Advances in Networks
JF  - Advances in Networks
JO  - Advances in Networks
SP  - 8
EP  - 15
PB  - Science Publishing Group
SN  - 2326-9782
UR  - https://doi.org/10.11648/j.net.20190701.12
AB  - The Internet of Things (IoT) is now infiltrating into our daily lives, providing important measurement and collection tools to inform us of every decision. Millions of sensors and devices continue to generate data and exchange important information through complex networks that support machine-to-machine communication and monitor and control critical smart world infrastructure. As a strategy to alleviate resource congestion escalation, edge computing has become a new paradigm for addressing the needs of the Internet of Things and localization computing. Compared to well-known cloud computing, edge computing migrates data calculations or storage to the edge of the network near the end-user. Thus, multiple compute nodes distributed across the network can offload computational pressure from a centralized data center and can significantly reduce latency in message exchanges. Besides, the distributed architecture balances network traffic and avoids spikes in traffic in the IoT network, reduces latency between edge/cloud servers and end-users, and reduces response time for real-time IoT applications compared to traditional cloud services. In this article, we conducted a comprehensive survey to analyze how edge computing can improve the performance of IoT networks. We classify edge calculations into different groups based on the architecture and study their performance by comparing network latency, bandwidth usage, power consumption, and overhead. Through the systematic introduction of the concept of edge computing, typical application scenarios, research status, and key technologies, it is considered that the development of edge computing is still in the initial stage. There are still many problems in practical applications that need to be solved, including optimizing edge computing performance, security, interoperability, and intelligent edge operations management services.
VL  - 7
IS  - 1
ER  -

Copy | Download