Cost-Efficient Data Offloading and Resource Allocation in 6G Space-Air-Ground Integrated IoT Networks

With the rapid development of the Internet of Things (IoT), 6G space-air-ground integrated networks (SAGIN) have emerged to provide key solutions for wide-area coverage, data management and resource allocation. This paper presents an efficient data offloading and resource allocation strategy for 6G SAGIN assisted by Unmanned Aerial Vehicle (UAV) and Low Earth Orbit (LEO) satellite. In this paper, our goal is to minimize the total system cost, which is the weighted sum of latency and energy consumption, under UAV storage capacity constraint. Due to the complexity of optimization problem, we decompose the original problem into two sub-problems: the computation resource allocation sub-problem and the data offloading and transmission power allocation sub-problem. To address the first sub-problem, we demonstrate its convex optimization nature and propose the Newton Interior-Point Method (IPM)-based Computation resource Allocation (NICA) algorithm. For the second sub-problem, we introduce the Genetic Algorithm-based UAV Data Offloading and transmission Power Allocation (GADOPA) algorithm. Different offloading methods are provided for different types of data (i.e., computational and collected data) to meet their differentiated needs. Through simulation experiments, we demonstrate the effectiveness of our proposed methods.