  Mobile edge computing (MEC) has been considered as a promising technique for
internet of things (IoT). By deploying edge servers at the proximity of
devices, it is expected to provide services and process data at a relatively
low delay by intelligent networking. However, the vast edge servers may face
great challenges in terms of cooperation and resource allocation. Furthermore,
intelligent networking requires online implementation in distributed mode. In
such kinds of systems, the network scheduling can not follow any previously
known rule due to complicated application environment. Then statistical
learning rises up as a promising technique for network scheduling, where edges
dynamically learn environmental elements with cooperations. It is expected such
learning based methods may relieve deficiency of model limitations, which
enhance their practical use in dynamic network scheduling. In this paper, we
investigate the vision and challenges of the intelligent IoT networking with
mobile edge computing. From the systematic viewpoint, some major research
opportunities are enumerated with respect to statistical learning.
