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Edge4Sys: a device-edge collaborative framework for MEC based smart systems

Authors:

Han Gao,

Yi Xu,

Xiao Liu,

Jia Xu,

Tianxiang Chen,

Bowen Zhou,

Rui Li,

Xuejun LiAuthors Info & Claims

ASE '20: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

Pages 1252 - 1254

https://doi.org/10.1145/3324884.3418908

Published: 27 January 2021 Publication History

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Abstract

At present, most of the smart systems are based on cloud computing, and massive data generated at the smart end device will need to be transferred to the cloud where AI models are deployed. Therefore, a big challenge for smart system engineers is that cloud based smart systems often face issues such as network congestion and high latency. In recent years, mobile edge computing (MEC) is becoming a promising solution which supports computation-intensive tasks such as deep learning through computation offloading to the servers located at the local network edge. To take full advantage of MEC, an effective collaboration between the end device and the edge server is essential. In this paper, as an initial investigation, we propose Edge4Sys, a Device-Edge Collaborative Framework for MEC based Smart System. Specifically, we employ the deep learning based user identification process in a MEC-based UAV (Unmanned Aerial Vehicle) delivery system as a case study to demonstrate the effectiveness of the proposed framework which can significantly reduce the network traffic and the response time.

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Cited By

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Liu ZSong JQiu CWang XChen XHe QSheng H(2024)Hastening Stream Offloading of Inference via Multi-Exit DNNs in Mobile Edge ComputingIEEE Transactions on Mobile Computing10.1109/TMC.2022.321872423:1(535-548)Online publication date: Jan-2024
https://doi.org/10.1109/TMC.2022.3218724
Xu YLuan FKua JLuo HWang ZLiu X(2024)Multi-UAV Collaborative Face Recognition for Goods Receiver in Edge-Based Smart Delivery ServicesAlgorithms and Architectures for Parallel Processing10.1007/978-981-97-0859-8_13(217-235)Online publication date: 27-Feb-2024
https://doi.org/10.1007/978-981-97-0859-8_13
Xu YGuo RKua JLuo HZhang ZLiu X(2024)We Will Find You: An Edge-Based Multi-UAV Multi-Recipient Identification Method in Smart Delivery ServicesAlgorithms and Architectures for Parallel Processing10.1007/978-981-97-0859-8_10(160-173)Online publication date: 27-Feb-2024
https://doi.org/10.1007/978-981-97-0859-8_10
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Index Terms

Edge4Sys: a device-edge collaborative framework for MEC based smart systems
1. Computing methodologies
  1. Artificial intelligence

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Published In

ASE '20: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

December 2020

1449 pages

ISBN:9781450367684

DOI:10.1145/3324884

General Chair:
John Grundy,
Program Chairs:
Claire Le Goues,
David Lo

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 27 January 2021

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ASE '20: 35th IEEE/ACM International Conference on Automated Software Engineering

December 21 - 25, 2020

Virtual Event, Australia

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Overall Acceptance Rate 82 of 337 submissions, 24%

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Liu ZSong JQiu CWang XChen XHe QSheng H(2024)Hastening Stream Offloading of Inference via Multi-Exit DNNs in Mobile Edge ComputingIEEE Transactions on Mobile Computing10.1109/TMC.2022.321872423:1(535-548)Online publication date: Jan-2024
https://doi.org/10.1109/TMC.2022.3218724
Xu YLuan FKua JLuo HWang ZLiu X(2024)Multi-UAV Collaborative Face Recognition for Goods Receiver in Edge-Based Smart Delivery ServicesAlgorithms and Architectures for Parallel Processing10.1007/978-981-97-0859-8_13(217-235)Online publication date: 27-Feb-2024
https://doi.org/10.1007/978-981-97-0859-8_13
Xu YGuo RKua JLuo HZhang ZLiu X(2024)We Will Find You: An Edge-Based Multi-UAV Multi-Recipient Identification Method in Smart Delivery ServicesAlgorithms and Architectures for Parallel Processing10.1007/978-981-97-0859-8_10(160-173)Online publication date: 27-Feb-2024
https://doi.org/10.1007/978-981-97-0859-8_10
Li XChen TYuan DXu JLiu X(2023)A Novel Graph-Based Computation Offloading Strategy for Workflow Applications in Mobile Edge ComputingIEEE Transactions on Services Computing10.1109/TSC.2022.318006716:2(845-857)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TSC.2022.3180067
Luo HChen TLi XLi SZhang CZhao GLiu X(2023)KeepEdge: A Knowledge Distillation Empowered Edge Intelligence Framework for Visual Assisted Positioning in UAV DeliveryIEEE Transactions on Mobile Computing10.1109/TMC.2022.315795722:8(4729-4741)Online publication date: 1-Aug-2023
https://doi.org/10.1109/TMC.2022.3157957
Li WLi QChen LWu FRen J(2022)A Storage Resource Collaboration Model Among Edge Nodes in Edge Federation ServiceIEEE Transactions on Vehicular Technology10.1109/TVT.2022.317936371:9(9212-9224)Online publication date: Sep-2022
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Zhang YLiu XXu JYuan DLi X(2022)A Novel Adaptive Computation Offloading Strategy for Collaborative DNN Inference over Edge Devices2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00055(378-385)Online publication date: Dec-2022
https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00055
Rathee GKhelifi AIqbal R(2021)Artificial Intelligence‐ (AI‐) Enabled Internet of Things (IoT) for Secure Big Data Processing in Multihoming NetworksWireless Communications and Mobile Computing10.1155/2021/57543222021:1Online publication date: 15-Aug-2021
https://doi.org/10.1155/2021/5754322

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A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms: Transparent Computing, Mobile Edge Computing, Fog Computing, and Cloudlet

All one needs to know about fog computing and related edge computing paradigms: A complete survey

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