  In this paper, we consider a hybrid mobile edge computing (H-MEC) platform,
which includes ground stations (GSs), ground vehicles (GVs) and unmanned aerial
vehicle (UAVs), all with mobile edge cloud installed to enable user equipments
(UEs) or Internet of thing (IoT) devices with intensive computing tasks to
offload. Our objective is to obtain an online offloading algorithm to minimize
the energy consumption of all the UEs, by jointly optimizing the positions of
GVs and UAVs, user association and resource allocation in real-time, while
considering the dynamic environment. To this end, we propose a hybrid deep
learning based online offloading (H2O) framework where a large-scale path-loss
fuzzy c-means (LSFCM) algorithm is first proposed and used to predict the
optimal positions of GVs and UAVs. Secondly, a fuzzy membership matrix U-based
particle swarm optimization (U-PSO) algorithm is applied to solve the mixed
integer nonlinear programming (MINLP) problems and generate the sample datasets
for the deep neural network (DNN) where the fuzzy membership matrix can capture
the small-scale fading effects and the information of mutual interference.
Thirdly, a DNN with the scheduling layer is introduced to provide user
association and computing resource allocation under the practical latency
requirement of the tasks and limited available computing resource of H-MEC. In
addition, different from traditional DNN predictor, we only input one UE
information to the DNN at one time, which will be suitable for the scenarios
where the number of UE is varying and avoid the curse of dimensionality in DNN.
