DDPG-Based Computation Offloading Strategy for Maritime UAV

With the development of the maritime Internet of Things (MIoT), a large number of sensors are deployed, generating massive amounts of data. However, due to the limited data processing capabilities of the sensors and the constrained service capacity of maritime communication networks, the local and cloud data processing of MIoT are restricted. Thus, there is a pressing demand for efficient edge-based data processing solutions. In this paper, we investigate unmanned aerial vehicle (UAV)-assisted maritime edge computing networks. Under energy constraints of both UAV and MIoT devices, we propose a Deep Deterministic Policy Gradient (DDPG)-based maritime computation offloading and resource allocation algorithm to efficiently process MIoT tasks current form of UAV. The algorithm jointly optimizes task offloading ratios, UAV trajectory planning, and edge computing resource allocation to minimize total system task latency while satisfying energy consumption constraints. Simulation results validate its effectiveness and robustness in highly dynamic maritime environments.