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Mobile edge computing (MEC) has been considered as a promising technique forinternet of things (IoT). By deploying edge servers at the proximity ofdevices, it is expected to provide services and process data at a relativelylow delay by intelligent networking. However, the vast edge servers may facegreat challenges in terms of cooperation and resource allocation. Furthermore,intelligent networking requires online implementation in distributed mode. Insuch kinds of systems, the network scheduling can not follow any previouslyknown rule due to complicated application environment. Then statisticallearning rises up as a promising technique for network scheduling, where edgesdynamically learn environmental elements with cooperations. It is expected suchlearning based methods may relieve deficiency of model limitations, whichenhance their practical use in dynamic network scheduling. In this paper, weinvestigate the vision and challenges of the intelligent IoT networking withmobile edge computing. From the systematic viewpoint, some major researchopportunities are enumerated with respect to statistical learning.

Intelligent networking with Mobile Edge Computing: Vision and Challenges for Dynamic Network Scheduling