Advances in Networks

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

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.

DOI 10.11648/j.net.20190701.12
Published in Advances in Networks (Volume 7, Issue 1, June 2019)
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), 2024. Published by Science Publishing Group

Keywords

Edge Computing, Security, Interoperability

References
[1] Satyanarayanan M. Edge Computing [J]. Computer, 2017, 50 (10): 36-38.
[2] Satyanarayanan M. The Emergence of Edge Computing [J]. Computer, 2017, 50 (1): 30-39.
[3] Feng J, Zhi L, Wu C, et al. AVE: Autonomous Vehicular Edge Computing Framework with ACO-Based Scheduling [J]. IEEE Transactions on Vehicular Technology, 2017, PP (99): 1-1.
[4] Ananthanarayanan G, Bahl P, Bodik P, et al. Real-Time Video Analytics: The Killer App for Edge Computing [J]. Computer, 2017, 50 (10): 58-67.
[5] Y. Wu, Y. Liu, S. H. Ahmed, J. Peng, and A. A. A. El-Latif, “Dominant dataset selection algorithms for electricity consumption time-series data analysis based on affine transformation,” IEEE Internet of Things Journal, pp. 1–1, 2019.
[6] Tuyen Xuan Tran, Pompili D. Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks [J]. 2017, PP (99): 1-1.
[7] Feng W, Jie X, Xin W, et al. Joint Offloading and Computing Optimization in Wireless Powered Mobile-Edge Computing Systems [J]. IEEE Transactions on Wireless Communications, 2017, PP (99): 1-1.
[8] Mao Y, Zhang J, Song S H, et al. Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems [J]. IEEE Transactions on Wireless Communications, 2017, 16 (9): 5994-6009.
[9] Hsu R H, Lee J, Quek T Q S, et al. Reconfigurable Security: Edge Computing-based Framework for IoT [J]. IEEE Network, 2017, 32 (5).
[10] Kai W, Hao Y, Wei Q, et al. Enabling Collaborative Edge Computing for Software Defined Vehicular Networks [J]. IEEE Network, 2018, 32 (5): 1-6.
[11] Y. Liu, J. Peng, J. J. Yu, and Y. Wu, “Ppgan: Privacy-preserving generative adversarial network,” arXiv preprint arXiv:1910.02007, 2019
[12] Kang J, Yu R, Huang X, et al. Enabling Localized Peer-to-Peer Electricity Trading Among Plug-in Hybrid Electric Vehicles Using Consortium Blockchains [J]. IEEE Transactions on Industrial Informatics, 2017, PP (99):1-1.
[13] Tran T X, Hajisami A, Pandey P, et al. Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges [J]. IEEE Communications Magazine, 2017, 55 (4): 54-61.
[14] Kang J, Xiong Z, Niyato D, et al. Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach [J]. 2019.
[15] Hong L, Yan Z, Tao Y. Blockchain-Enabled Security in Electric Vehicles Cloud and Edge Computing [J]. IEEE Network, 2018, 32 (3): 78-83.
[16] Rimal B P, Van D P, Maier M. Cloudlet Enhanced Fiber-Wireless Access Networks for Mobile-Edge Computing [J]. IEEE Transactions on Wireless Communications, 2017, PP (99): 1-1.
[17] Kang J, Xiong Z, Niyato D, et al. Toward Secure Blockchain-Enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory [J]. IEEE Transactions on Vehicular Technology, 2019, PP (99):1-1.
[18] Li Z, Kang J, Yu R, et al. Consortium Blockchain for Secure Energy Trading in Industrial Internet of Things [J]. IEEE Transactions on Industrial Informatics, 2017:1-1.
[19] Sun Y, Guo X, Song J, et al. Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems [J]. 2019.
[20] Zhang Yanjun, Yang Xiaodong, Liu Yi, Zheng Dayuan, Bi Shujun. Research on the Frame of Intelligent Inspection Platform Based on Spatio-temporal Data. Computer & Digital Engineering [J], 2019, 47 (03): 616-619+637.
[21] Z. Zhao, J. Wang and Y. Liu, "User Electricity Behavior Analysis Based on K-Means Plus Clustering Algorithm," 2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC), Dalian, China, 2017, pp. 484-487. doi: 10.1109/ICCTEC.2017.00111.
[22] Liu Y, Yang C, Jiang L, et al. Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities [J]. IEEE Network, 2019, 33 (2): 111-117.
[23] Lin M, Chen Z, Liao H, et al. ELECTRE II method to deal with probabilistic linguistic term sets and its application to edge computing [J]. Nonlinear Dynamics, 2019 (3): 1-19.
[24] Ping Z, Durresi M, Durresi A. Multi-access edge computing aided mobility for privacy protection in Internet of Things [J]. Computing, 2018 (10): 1-14.
[25] Wang R, Yan J, Wu D, et al. Knowledge-Centric Edge Computing Based on Virtualized D2D Communication Systems [J]. IEEE Communications Magazine, 2018, 56 (5): 32-38.
[26] Yi Liu, Jiawen Peng, and Zhihao Yu. 2018. Big Data Platform Architecture under The Background of Financial Technology: In The Insurance Industry As An Example. In Proceedings of the 2018 International Conference on Big Data Engineering and Technology (BDET 2018). ACM, New York, NY, USA, 31-35.
[27] Chen C H, Lin M Y, Liu C C. Edge Computing Gateway of the Industrial Internet of Things Using Multiple Collaborative Microcontrollers [J]. IEEE Network, 2018, 32 (1): 24-32.
[28] Huang X, Yu R, Kang J, et al. Exploring Mobile Edge Computing for 5G-Enabled Software Defined Vehicular Networks [J]. IEEE Wireless Communications, 2017, 24 (6): 55-63.
[29] Kang J, Yu R, Huang X, et al. Privacy-Preserved Pseudonym Scheme for Fog Computing Supported Internet of Vehicles [J]. IEEE Transactions on Intelligent Transportation Systems, 2017:1-11.
[30] Huang X, Yu R, Kang J, et al. Distributed Reputation Management for Secure and Efficient Vehicular Edge Computing and Networks [J]. IEEE Access, 2017, PP (99): 1-1.
[31] Zhou F, Wu Y, Hu R Q, et al. Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems [J]. IEEE Journal on Selected Areas in Communications, 2018, PP (99): 1-1.
[32] Huang X, Yu R, Kang J, et al. Software Defined Energy Harvesting Networking for 5G Green Communications [J]. IEEE Wireless Communications, 2017, 24 (4):38-45.
[33] Ju R, Yi P, Goscinski A, et al. Edge Computing for the Internet of Things [J]. IEEE Network, 2018, 32 (1): 6-7.
[34] Muhammad G, Alhamid M F, Alsulaiman M, et al. Edge Computing with Cloud for Voice Disorder Assessment and Treatment [J]. IEEE Communications Magazine, 2018, 56 (4): 60-65.
[35] Suganuma T, Oide T, Kitagami S, et al. Multiagent-Based Flexible Edge Computing Architecture for IoT [J]. IEEE Network, 2018, 32 (1): 16-23.
[36] Wei L, Yang T, Delicato F C, et al. On Enabling Sustainable Edge Computing with Renewable Energy Resources [J]. IEEE Communications Magazine, 2018, 56 (5): 94-101.
[37] Wang, Fanglin, Jialiang Peng, and Yongjie Li. "Hypergraph based feature fusion for 3-D object retrieval." Neurocomputing 151 (2015): 612-619.
[38] Li S, Ning Z, Lin S, et al. Joint Admission Control and Resource Allocation in Edge Computing for Internet of Things [J]. IEEE Network, 2018, 32 (1): 72-79.
[39] Kang J, Yu R, Huang X, et al. Location privacy attacks and defenses in cloud-enabled internet of vehicles [J]. IEEE Wireless Communications, 2016, 23 (5): 52-59.
[40] Mehrabi A, Siekkinen M, Yla-Jaaski A. Edge Computing Assisted Adaptive Mobile Video Streaming [J]. IEEE Transactions on Mobile Computing, 2018, PP (99): 1-1.
[41] Cao J, Castiglione A, Motta G, et al. Human-Driven Edge Computing and Communication: Part 2 [J]. IEEE Communications Magazine, 2018, 56 (2): 134-135.
[42] Yu R, Kang J, Huang X, et al. MixGroup: Accumulative Pseudonym Exchanging for Location Privacy Preservation in Vehicular Social Networks [J]. IEEE Transactions on Dependable and Secure Computing, 2015:1-1.
Author Information
  • College of Information Engineering, Harbin Institute of Petroleum, Harbin, China

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  • 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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    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://download.sciencepg.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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    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
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    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.
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