  Edge computing-enhanced Internet of Vehicles (EC-IoV) enables ubiquitous data
processing and content sharing among vehicles and terrestrial edge computing
(TEC) infrastructures (e.g., 5G base stations and roadside units) with little
or no human intervention, plays a key role in the intelligent transportation
systems. However, EC-IoV is heavily dependent on the connections and
interactions between vehicles and TEC infrastructures, thus will break down in
some remote areas where TEC infrastructures are unavailable (e.g., desert,
isolated islands and disaster-stricken areas). Driven by the ubiquitous
connections and global-area coverage, space-air-ground integrated networks
(SAGINs) efficiently support seamless coverage and efficient resource
management, represent the next frontier for edge computing. In light of this,
we first review the state-of-the-art edge computing research for SAGINs in this
article. After discussing several existing orbital and aerial edge computing
architectures, we propose a framework of edge computing-enabled
space-air-ground integrated networks (EC-SAGINs) to support various IoV
services for the vehicles in remote areas. The main objective of the framework
is to minimize the task completion time and satellite resource usage. To this
end, a pre-classification scheme is presented to reduce the size of action
space, and a deep imitation learning (DIL) driven offloading and caching
algorithm is proposed to achieve real-time decision making. Simulation results
show the effectiveness of our proposed scheme. At last, we also discuss some
technology challenges and future directions.
