Spatial Crowdsourcing-based Task Allocation for UAV-assisted Maritime Data Collection

Abstract--Driven by the unceasing development of maritime services, tasks of unmanned aerial vehicle (UA V)-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 SCbased MDC network model and investigate the task allocation problem for UA V-assisted MDC. In variable maritime service scenarios, tasks are allocated to UA Vs based on the spatial and temporal requirements of the tasks, as well as the mobility of the UA Vs. T o address this problem, we design an SCbased task allocation algorithm for the MDC (SC-MDC-T A). The quality estimation is utilized to assess and regulate task execution quality by evaluating signal to interference plus noise ratio and the UA V 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-T A algorithm effectively allocates tasks for various MDC scenarios. Furthermore, compared to the benchmark, the SC-MDC-T A algorithm can also reduce the task completion time and lower the UA V energy consumption. RIVEN by the continuous development of maritime services such as marine resources exploration, reconnaissance and surveillance, anti-submarine, marine tourism, marine transportation and emergency collection, tasks of unmanned aerial vehicle (UA V)-assisted maritime data collection (MDC) are becoming increasingly diverse, complex and personalized [1]-[3]. Personal use of this material is permitted.