With the continuous increment of maritime applications, the development of
marine networks for data offloading becomes necessary. However, the limited
maritime network resources are very difficult to satisfy real-time demands.
Besides, how to effectively handle multiple compute-intensive tasks becomes
another intractable issue. Hence, in this paper, we focus on the decision of
maritime task offloading by the cooperation of unmanned aerial vehicles (UAVs)
and vessels. Specifically, we first propose a cooperative offloading framework,
including the demands from marine Internet of Things (MIoTs) devices and
resource providers from UAVs and vessels. Due to the limited energy and
computation ability of UAVs, it is necessary to help better apply the vessels
to computation offloading. Then, we formulate the studied problem into a Markov
decision process, aiming to minimize the total execution time and energy cost.
Then, we leverage Lyapunov optimization to convert the long-term constraints of
the total execution time and energy cost into their short-term constraints,
further yielding a set of per-time-slot optimization problems. Furthermore, we
propose a Q-learning based approach to solve the short-term problem
efficiently. Finally, simulation results are conducted to verify the
correctness and effectiveness of the proposed algorithm.

本文提出了一种合作卸载框架，通过船舶和无人机协作来处理海事任务卸载问题，并使用马尔科夫决策过程来最小化总执行时间和能源成本，通过 Lyapunov 优化将长期限制转化为短期限制，并提出了一种基于 Q-learning 的方法来有效地解决短期问题。