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.

翻译：随着海事应用的持续增长，构建用于数据卸载的海洋网络变得必要。然而，有限的海洋网络资源极难满足实时需求。此外，如何有效处理多个计算密集型任务成为另一个棘手问题。因此，本文聚焦于通过无人机与船舶协同进行海事任务卸载决策。具体而言，我们首先提出一个协同卸载框架，涵盖海洋物联网设备的请求以及无人机与船舶的资源供给。考虑到无人机能量与计算能力有限，有必要更充分利用船舶进行计算卸载。随后，我们将所研究问题建模为马尔可夫决策过程，旨在最小化总执行时间与能量成本。接着，采用李雅普诺夫优化将总执行时间与能量成本的长期约束转化为短期约束，进而得到一系列单时隙优化问题。在此基础上，提出基于Q学习的方法高效求解短期问题。最后，通过仿真结果验证了所提算法的正确性与有效性。