LAUSR

Space–air–ground integrated edge computing is expecting to provide pervasive computation services for Internet of Things (IoT), especially in remote areas. However, the offloading process of power-limited IoT devices is a challenge issue due to unreliable communications in an aerial environment. In this article, we propose an energy-efficient space–air–ground integrated edge computing network architecture, in which the IoT devices choose the most appropriate LEO satellites or unmanned aerial vehicles (UAVs) for task offloading according to their energy level, communication conditions and computing capabilities. In order to providing efficient task offloading and energy-saving policy under an uncertainty aerial environment, a constrained Markov decision process is employed to formulate the task offloading decision problem and a deep reinforcement learning (DRL)-based algorithm is devised to solve the proposed problem. An adaptive federated DRL-based offloading method is further proposed to find suboptimal offloading decisions by considering the privacy protection and communication failure in the proposed network. Numerical results confirm the effectiveness of the proposed schemes on energy saving and computation efficiency.