Cooperative content offloading scheme in air-ocean integrated networks

As one of the most promising networks, the air-ocean integrated networks (AOINs) composed of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) meet a variety of requests from different maritime missions with the characteristics of seamless, high-rate, and reliable transmission. However, due to the limited storage capacities of UAVs and uncertain navigation paths of USVs, it is challenging to accomplish the ocean observation mission. In this paper, a UAV and USV cooperative content offloading scheme in AOINs with Q-learning and game theory is proposed. Specifically, the state evaluation mechanism for both UAV and USV is first designed to make cooperation strategies. Afterward, the interaction between UAV and USV is modeled as the bargaining game, where the Nash equilibrium as the optimal transaction price is obtained by the backward induction method. To realize the maximization of revenue, we devise a Q-learning based algorithm to make path planning for each USV to offload contents as many as possible under the limited energy. Finally, the effectiveness and efficiency of the proposed scheme is conducted by extensive simulations.