Open AccessPosted Content
A Survey on Mobile Edge Computing: The Communication Perspective
TLDR
A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management and recent standardization efforts on MEC are introduced.Abstract:
Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.read more
Citations
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A Survey on the Edge Computing for the Internet of Things
TL;DR: A comprehensive survey, analyzing how edge computing improves the performance of IoT networks and considers security issues in edge computing, evaluating the availability, integrity, and the confidentiality of security strategies of each group, and proposing a framework for security evaluation of IoT Networks with edge computing.
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Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
TL;DR: A comprehensive survey of the recent research efforts on edge intelligence can be found in this paper, where the authors review the background and motivation for AI running at the network edge and provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the edge.
Journal ArticleDOI
Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts
Xiaohu You,Cheng-Xiang Wang,Jie Huang,Xiqi Gao,Zaichen Zhang,Michael Mao Wang,Yongming Huang,Chuan Zhang,Yanxiang Jiang,Jiaheng Wang,Min Zhu,Bin Sheng,Dongming Wang,Zhiwen Pan,Pengcheng Zhu,Yang Yang,Zening Liu,Ping Zhang,Xiaofeng Tao,Shaoqian Li,Zhi Chen,Xinying Ma,Chih-Lin I,Shuangfeng Han,Ke Li,Pan Chengkang,Zhiming Zheng,Lajos Hanzo,Xuemin Shen,Yingjie Jay Guo,Zhiguo Ding,Harald Haas,Wen Tong,Peiying Zhu,Guanghua Yang,Jun Wang,Eric G. Larsson,Hien Quoc Ngo,Wei Hong,Haiming Wang,Debin Hou,Jixin Chen,Zhe Chen,Zhang-Cheng Hao,Geoffrey Ye Li,Rahim Tafazolli,Yue Gao,H. Vincent Poor,Gerhard P. Fettweis,Ying-Chang Liang +49 more
TL;DR: 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.
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Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network
TL;DR: This paper investigates the task offloading problem in ultra-dense network aiming to minimize the delay while saving the battery life of user’s equipment and proposes an efficient offloading scheme which can reduce 20% of the task duration with 30% energy saving.
Journal ArticleDOI
Deep Learning With Edge Computing: A Review
Jiasi Chen,Xukan Ran +1 more
TL;DR: This paper will provide an overview of applications where deep learning is used at the network edge, discuss various approaches for quickly executing deep learning inference across a combination of end devices, edge servers, and the cloud, and describe the methods for training deep learning models across multiple edge devices.
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