Driven by the unceasing development of maritime services, tasks of unmanned aerial vehicle (UAV)-assisted maritime data collection (MDC) are becoming increasingly diverse, complex and personalized. As a result, effective task allocation for MDC is becoming increasingly critical. In this work, integrating the concept of spatial crowdsourcing (SC), we develop an SC-based MDC network model and investigate the task allocation problem for UAV-assisted MDC. In variable maritime service scenarios, tasks are allocated to UAVs based on the spatial and temporal requirements of the tasks, as well as the mobility of the UAVs. To address this problem, we design an SC-based task allocation algorithm for the MDC (SC-MDC-TA). The quality estimation is utilized to assess and regulate task execution quality by evaluating signal to interference plus noise ratio and the UAV energy consumption. The reverse auction is employed to potentially reduce the task waiting time as much as possible while ensuring timely completion. Additionally, we establish typical task allocation scenarios based on maritime service requirements indicated by electronic navigational charts. Simulation results demonstrate that the proposed SC-MDC-TA algorithm effectively allocates tasks for various MDC scenarios. Furthermore, compared to the benchmark, the SC-MDC-TA algorithm can also reduce the task completion time and lower the UAV energy consumption.

翻译：随着海上服务的不断发展，无人机辅助海上数据采集任务日益多样化、复杂化和个性化。因此，有效的海上数据采集任务分配变得愈发关键。本研究结合空间众包理念，构建了基于空间众包的海上数据采集网络模型，并探究了无人机辅助海上数据采集的任务分配问题。在多变的海上服务场景中，任务根据其时空需求及无人机的移动性分配给无人机。为解决该问题，我们设计了基于空间众包的海上数据采集任务分配算法。该算法通过评估信干噪比和无人机能耗，利用质量估计来评估和调控任务执行质量；采用反向拍卖机制，在确保任务及时完成的前提下尽可能缩短任务等待时间。此外，我们基于电子海图指示的海上服务需求建立了典型任务分配场景。仿真结果表明，所提出的SC-MDC-TA算法能有效适应多种海上数据采集场景的任务分配。与基准方法相比，该算法还能缩短任务完成时间并降低无人机能耗。