A joint strategy for service deployment and task offloading in satellite–terrestrial IoT

In recent years, low earth orbit satellite constellations, which are an important component of 6G, have been considered as a potential solution to achieve seamless network services for remote areas. Service deployment based on network function virtualization (NFV) has become an essential trend among satellite networks to enable flexible network services. However, current satellite–terrestrial IoT task offloading schemes rarely consider NFV-based satellite service deployment, which limits the performance of satellite networks. In this study, we address this problem by proposing an optimization problem that jointly considers service deployment and task offloading. To solve such a problem with many coupling decision variables, we decouple the problem using a two-stage approach. We propose a deep reinforcement learning-based service deployment policy to solve the service deployment subproblem and an alternating direction multiplier method-based distributed approach to solve the task offloading subproblem, which with the aim of minimizing the task latency and energy consumption of IoT devices. Simulation experiments demonstrate that our scheme can obtain near-optimal solution and can be adapted to large-scale satellite–terrestrial IoT network scenarios.