Latency Optimization for Hybrid GEO–LEO Satellite-Assisted IoT Networks

Abstract

Benefiting from the development of satellite onboard processing capability, the orbital computing can be realized by deploying edge computing servers on satellites to reduce the task processing latency. However, edge computing based on geostationary earth orbit (GEO) or low earth orbit (LEO) alone can hardly meet the latency requirements of satellite assisted internet of things (SIoT) services. Moreover, the uneven distribution of tasks generated by SIoT devices will also cause the load unbalancing among different satellites. In this paper, hybrid GEO-LEO SIoT networks is investigated with joint computing and communication resource allocation. To tackle the load unbalancing problem, tasks generated by SIoT devices can be processed by collaborative LEO satellites or forwarded to gateways on ground via GEO satellite. Thus, the joint task offloading, communication and computing resources allocation for the hybrid SIoT network can be formulated as a mixed integer dynamic programming problem with satellites-ground cooperation and inter-satellite cooperation via the inter-satellite links. Then, an intelligent task offloading and multi-dimensional resources allocation algorithm (TOMRA) is proposed to minimize the latency of task offloading and processing. Firstly, a method base on deep reinforcement learning is utilized to solve the subproblem of task offloading and channel allocation. And then, convex optimization is adopted to solve the sub-problem of computing resource allocation under fixed offloading and channel allocation decisions. Simulation results show that the proposed TOMRA can achieve better performance than the reference schemes.