  By taking full advantage of Computing, Communication and Caching (3C)
resources at the network edge, Mobile Edge Computing (MEC) is envisioned as one
of the key enablers for the next generation networks. However, current
fixed-location MEC architecture may not be able to make real-time decision in
dynamic environment, especially in large-scale scenarios. To address this
issue, in this paper, a Heterogeneous MEC (H-MEC) architecture is proposed,
which is composed of fixed unit, i.e., Ground Stations (GSs) as well as moving
nodes, i.e., Ground Vehicles (GVs) and Unmanned Aerial Vehicles (UAVs), all
with 3C resource enabled. The key challenges in H-MEC, i.e., mobile edge node
management, real-time decision making, user association and resource allocation
along with the possible Artificial Intelligence (AI)-based solutions are
discussed. In addition, the AI-based joint Resource schEduling (ARE) framework
with two different AI-based mechanisms, i.e., Deep neural network (DNN)-based
and deep reinforcement learning (DRL)-based architectures are proposed.
DNN-based solution with online incremental learning applies the global
optimizer and therefore has better performance than the DRL-based architecture
with online policy updating, but requires longer training time. The simulation
results are given to verify the efficiency of our proposed ARE framework.
