Joint UAV Trajectory Planning and LEO Satellite Selection for Data Offloading in Space-Air-Ground Integrated Networks

TL;DR

This paper proposes a hierarchical data offloading framework in SAGIN, optimizing UAV trajectories and satellite selection to reduce energy consumption for IoT data collection and processing.

Contribution

It introduces a two-phase optimization approach for UAV trajectory planning and LEO satellite selection in SAGIN, addressing the challenges of heterogeneous and dynamic communication environments.

Findings

Achieved approximately 10% reduction in energy consumption compared to benchmarks.

Developed a real-time satellite selection mechanism using Satellite Tool Kit.

Effectively optimized UAV trajectories and IoT pairing for energy-efficient data offloading.

Abstract

With the development of low earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs), the space-air-ground integrated network (SAGIN) becomes a major trend in the next-generation networks. However, due to the instability of heterogeneous communication and time-varying characteristics of SAGIN, it is challenging to meet the remote Internet of Things (IoT) demands for data collection and offloading. In this paper, we investigate a two-phase hierarchical data uplink model in SAGIN. Specifically, UAVs optimize trajectories to enable efficient data collection from IoT devices, and then they transmit the data to LEO satellites with computing capabilities for further processing. The problem is formulated to minimize the total energy consumption for IoT devices, UAVs, and LEO satellites. Since the problem is in the form of mixed-integer nonlinear programming and intractable to solve directly, we decompose it into two phases. In the IoT-UAV phase, we design the algorithm to jointly optimize the IoT pairing, power allocation, and UAVs trajectories. Considering the high dynamic characteristics of LEO satellites, a real-time LEO satellite selection mechanism joint with the Satellite Tool Kit is proposed in the UAV-LEO phase. Finally, simulation results show the effectiveness of the proposed algorithms, with about 10% less energy consumption compared with the benchmark algorithm.