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

TL;DR

This paper proposes a novel spatial crowdsourcing-based task allocation algorithm for UAV-assisted maritime data collection, improving efficiency, reducing task completion time, and lowering UAV energy consumption in complex maritime scenarios.

Contribution

It introduces an SC-based task allocation model and algorithm tailored for UAV-assisted maritime data collection, addressing spatial-temporal requirements and UAV mobility.

Findings

The proposed SC-MDC-TA algorithm effectively allocates tasks across various scenarios.

It reduces task completion time compared to benchmark methods.

It lowers UAV energy consumption while maintaining timely task execution.

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.