Machine Learning in Mobile Edge Computing

 

In recent years, mobile edge computing has attracted a considerable amount of attention from both academia and industry through its many advantages (such as low latency, computation efficiency and privacy) caused by its local model of providing storage and computation resources. In addition, machine learning has become the dominant approach in applications such as industry, healthcare, smart home, and transportation. All of these applications heavily rely on technologies that can be deployed at the network edge. Therefore, it is essential to combine machine learning with mobile edge computing to further promote the proliferation of intelligent edges. In general, machine learning relies on powerful computation and storage resources for superior performance, while mobile edge computing typically provides limited computation resources locally.

 

To this end, the implementations of machine learning algorithms should be revisited for mobile edge computing. This special issue aims to become a valuable information source for state-of-the-art research and developments in machine learning for mobile edge computing and wireless mobile networks. It also aims to serve as an outlet for facilitating computational intelligence among mobile edge computing researchers, practitioners, and professionals across academia, government and industry. Finally, it aims to foster the dissemination of high quality research on new ideas, methods, theories, techniques, and applications of evaluation and management for improving mobile services. Original research articles are solicited in all aspects, including theoretical studies, practical applications, and experimental prototypes.

 

Submission Deadline: March 25, 2021

Acceptance Notification (1st round): May 25, 2021

Minor Revision Due: July 15, 2021

Final Decision Due: August 5, 2021

Final Manuscript Due: September 25, 2021

Publication Date: November 15, 2021

 

GUEST EDITORS 

Shangguang Wang, Beijing University of Post and Telecommunications, China

 

TOPICS OF INTEREST INCLUDE, BUT ARE NOT LIMITED TO, THE FOLLOWING: 
● Machine learning models in mobile edge computing 
● AI-assisted routing in mobile edge computing

● Blockchain-enabled 5/6G in mobile edge computing

● Edge intelligence and satellite edge computing

● Services and management in mobile edge computing 

● Human-machine integration in mobile edge computing

● Machine learning for XR in mobile edge computing

● Federated machine learning in mobile edge computing

● Compute first networking in mobile edge computing

● Tactile Internet and mobile edge computing 

China Communications seeks original, UNPUBLISHED research papers reporting substantive new work in various aspects of Information Centric Networking. Papers MUST clearly indicate your contributions to the field of Information Centric Networking and properly cite related work in this field.

 

Papers should be submitted in two separate .doc files (preferred) or .pdf files: 1) Main Document (including paper title, abstract, key words, and full text); 2) Title page (including paper title, author affiliation, acknowledgement and any other information related with the authors’ identification) through the Manuscript Central. Please register or login at http://mc03.manuscriptcentral.com/chinacomm, then go to the author center and follow the instructions there. Remember to select “Feature Topic: Machine Learning in Mobile Edge Computing - November Issue, 2021” as your manuscript type when submitting; otherwise, it might be considered as a regular paper.Each submission must be accompanied by the following information:

● an abstract of about 150 words

● 3-8 keywords

● original photographs with high-resolution (300 dpi or greater); eps. or tif. format is preferred

● sequentially numbered references. The basic reference format is: author name, "article name", issue name(italic）, vol, no., page, month, year. for example: Y.M Huang, "Pervateture in wireless hetergeneous..",IEEE Journal on Selected Areas, vol.27, no. 5, pp 34-50, May, 2009.

● brief biographies of authors (50-75 words)

● contact information, including email and mailing addresses