  Current maritime communications mainly rely on satellites having meager
transmission resources, hence suffering from poorer performance than modern
terrestrial wireless networks. With the growth of transcontinental air traffic,
the promising concept of aeronautical ad hoc networking relying on commercial
passenger airplanes is potentially capable of enhancing satellite-based
maritime communications via air-to-ground and multi-hop air-to-air links. In
this article, we conceive space-air-ground integrated networks (SAGINs) for
supporting ubiquitous maritime communications, where the low-earth-orbit
satellite constellations, passenger airplanes, terrestrial base stations,
ships, respectively, serve as the space-, air-, ground- and sea-layer. To meet
heterogeneous service requirements, and accommodate the time-varying and
self-organizing nature of SAGINs, we propose a deep learning (DL) aided
multi-objective routing algorithm, which exploits the quasi-predictable network
topology and operates in a distributed manner. Our simulation results based on
real satellite, flight, and shipping data in the North Atlantic region show
that the integrated network enhances the coverage quality by reducing the
end-to-end (E2E) delay and by boosting the E2E throughput as well as improving
the path-lifetime. The results demonstrate that our DL-aided multi-objective
routing algorithm is capable of achieving near Pareto-optimal performance.

Networking and Internet Architecture;Artificial Intelligence;Machine Learning