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

TL;DR

The paper tackles UAV-assisted maritime data collection (MDC) task allocation under diverse service scenarios by introducing a spatial crowdsourcing (SC) based MDC network model. It proposes the SC-MDC-TA algorithm, which integrates a SINR- and energy-aware quality estimation module and a reverse auction to minimize average task completion time while respecting UAV mobility, coverage, and energy constraints. The approach leverages a time-slotted matching process and a maritime work station to assign tasks based on spatial-temporal requirements and channel conditions, with explicit formulations for distance, SINR, and energy budgets. Simulation results on electronic navigational chart-based scenarios show substantial reductions in task completion time and UAV energy consumption compared with a benchmark closest-task allocation, demonstrating robustness across hover times, take-off positions, task distributions, and fleet sizes. The findings support the practical deployment of SC-based MDC task allocation to improve efficiency and reliability in maritime operations.

Abstract

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.

Figures (19)

• Figure 1: The proposed SC-based MDC network model.
• Figure 2: The porposed SC-based MDC task allocation procedure.
• Figure 3: Task positions and UAV positions in the electronic navigational charts.
• Figure 4: The MDC task allocation based on the proposed SC-MDC-TA algorithm under different hover time.
• Figure 5: The comparison of task completion time under different hover time.