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Publication Date

Fourth Quarter 2020

Manuscript Submission Deadline

Special Issue

Call for Papers

Driven by Internet of Things and proliferation of wireless services, wireless traffic has skyrocketed dramatically, causing potential congestion in core networks and severe strain on backhaul bandwidth. To address these challenges, mobile edge computing (MEC) provisions storage and computation resources in proximity of end devices, where the demands from end devices can be served locally in real-time, rather than being served by the remote cloud through latency plagued core network. Through decentralizing cloud service and spreading the burden of cloud servers in core networks to edge servers, the requests for both contents and computations can be satisfied locally, which significantly mitigates the pressure on core network and reduces the service latency for end devices.

The occurrence of MEC poses many new challenges in terms of deployment and management of distributed resources. By incorporating storage and computation resources in networks, joint allocation and management of communication, computing and storage resources will improve the quality of service and user experience, especially for latency-sensitive applications. However, it is very challenging to utilize heterogeneous resources in an efficient way to meet diverse service requirements of users. In addition, efficient orchestration of various servers with distinct capabilities and remote cloud is required to accommodate different services. Moreover, users are moving all the time and their requests also change over time. Last but not least, the system itself also exhibits high dynamics in terms of channel conditions, computation capacity and data storage, making management of MEC difficult. In such a complex and dynamic system, machine learning, artificial intelligence (AI), big data techniques can help provide insights and predictive analyses in real-time, and facilitate decision making to intelligently manage and control the system. Machine learning and AI combined with increased computing power will empower the edge systems to automatically adjust its strategy to achieve the optimal policy and make it extraordinarily intelligent.

The objective of this special issue is to explore recent advances in mobile edge computing systems and AI algorithms, to address the fundamental and practical challenges. This special issue will bring together leading researchers and developers from both industry and academia to present their research on intelligent mobile edge computing systems. This may include network modeling and architecture, AI enabled resource management, big data driven edge systems, orchestration of edge and cloud servers. High original research and review articles in this area are welcome. Potential topics include but are not limited to the following:

  • Intelligent deployment of MEC systems
  • Intelligent coordination of computation, communication, and caching
  • Deep learning aided orchestration of edge and cloud
  • Intelligent coding scheme for edge caching
  • Intelligent cognitive radio enabled MEC
  • Intelligent management of unmanned aerial vehicles(UAV)-enabled MEC
  • AI aided MEC for latency-aware applications
  • AI-enabled computation offloading
  • Deep learning for content popularity prediction and cache management
  • AI aided service mitigation and mobility management in MEC
  • Load prediction and traffic steering for MEC 
  • Multi-agent learning in MEC
  • Testbed and experiments of AI algorithms in MEC
  • Intelligent security and privacy provisioning

Submission Guidelines

Prospective authors should submit their manuscripts following the IEEE TCCN guidelines. Authors should submit a PDF version of their complete manuscript to Manuscript Central according to the following schedule:

Important Dates

Submission Deadline: 15 February 2020
First Reviews Complete: 1 May 2020
Revision Due: 15 June 2020
Final Review Decision: 15 August 2020
Final to Publisher: 1 September 2020
Publication: Fourth Quarter 2020

Guest Editors

Ning Zhang (Lead)
Texas A&M University at Corpus Christi, USA

Tao Han
University of North Carolina at Charlotte, USA

Hassan Aboubakr Omar
Huawei Inc., Ottawa, Canada

Xianfu Chen
VTT Technical Research Centre of Finland, Finland

Katsuya Suto
University of Electro-Communications, Tokyo, Japan

Xianbin Wang
Western University, Canada

Qinyu Zhang
Harbin Intuition of Technology (Shenzhen), China