Partitionable Task Offloading for Intelligent Ship-assisted Maritime Edge Computing Networks

Camera-equipped unmanned aerial vehicles (UAVs) can provide instant maritime visual data, playing a crucial role in maritime safety, environmental monitoring, and resource management. By integrating mobile edge computing (MEC) servers onboard UAVs, video data can be pre-processed and analyzed on-site, significantly reducing the volume of data transmission and response time. However, the limited computing capacities of UAVs pose significant challenges to the efficiency of task execution. Benefiting from the cooperation of multiple intelligent ships (ISs), the partitionable tasks with a large data size can be divided into multiple subtasks of different sizes and executed by different ISs. In this paper, we investigate the ISs assisted maritime computing (ISMC) network consisting of one UAV and multiple ISs. Then, we aim to minimize the maximum delay among all subtasks under the energy constraint of the UAV by jointly optimizing the division ratio of subtasks and the transmit power of the UAV. Since the formulated optimization problem is typically hard to solve due to its non-convexity, we propose an effective iterative algorithm by breaking it into two sub-problems. Numerical simulation results show the effectiveness of the proposed algorithm with various performance parameters.