Joint Optimization of Task Offloading and Resource Allocation in Satellite-Assisted IoT Networks

Abstract

Satellite edge computing can provide ubiquitous and reliable connectivity to remote or disaster area networks that are difficult to serve. However, due to the explosive growth of Internet-of-Things (IoT) data traffic, satellite edge services alone make it difficult to meet the latency and energy demands of abundant IoT devices. Edge learning, combined with edge computing and machine learning, is expected to be the key to solving this problem. This paper constructs a Satellite-assisted IoT network model consisting of terminals, satellites, and a cloud center. Terminals can offload tasks according to actual needs for load balancing. An edge learning approach using cloud edge collaboration is proposed. Then, for concurrent random tasks with different service demands, a minimization problem for the weighted sum of system latency and energy consumption is formulated. Finally, a Model-assisted Two-tier Reinforcement Learning (MTRL) optimization algorithm for task offloading decisions and resource allocation is proposed to solve this problem. Simulation results show that the proposed algorithm has a good performance in convergence. The system performance is better than existing algorithms, which can lead to a 27.5% reduction in the system cost. Furthermore, the proposed algorithm can lead to a smaller increase in system cost as the number of IoT devices increases.