Intelligent Task Offloading for 6G-Enabled Maritime IoT Based on Reinforcement Learning

With the continuous increase of maritime activities, maritime Internet of Things (M-IoT) devices are also growing explosively, which puts forward higher requirements for maritime networks with limited resources and complex environment. As the basis of 6G edge intelligence, edge computing technology can be adopted to deal with massive heterogeneous ocean data effectively to meet the needs of different maritime application scenarios. To improve the execution efficiency of maritime edge computing under the constraints of delay and energy consumption, we propose an edge computing network architecture for M-IoT based on the space-air-ground-sea integrated communication network of 6G, and put forward an intelligent task offloading algorithm based on Reinforcement Learning(RL), which migrates heavy computation tasks to suitable servers by means of cloud-edge-end cooperation. Simulation results show the superiority of the algorithm in time delay and energy efficiency.