Adaptive UAV Deployment for Remote IoT Computation Offloading in Integrated Space-Air-Grou...

In the realm of the Internet of Things (IoT), computation offloading confronts challenges in remote areas due to scarce general-purpose edge/cloud infrastructure and insufficient terrestrial network coverage. To address this, we introduce a novel space-air-ground integrated network (SAGIN) computing architecture, designed for the efficient offloading of computation-intensive applications. Within this architecture, unmanned aerial vehicles (UAVs) conduct edge computing near users, while satellites act as a bridge to cloud computing resources. Given the limitations of UAVs in terms of battery capacity and dynamic network topology, their deployment strategy is crucial for maintaining service quality. Due to the impracticality of collecting global user information for centralized control of UAVs, we have conducted research on the adaptive deployment of UAVs under the condition that they rely solely on local observations. We propose a multi-agent softmax deep double deterministic policy gradient (MASD3) algorithm and comprehensively consider maximizing the uplink transmission rate of terrestrial IoT devices and reducing the energy consumption of UAVs during flight and communication in the optimization objective. Simulation results demonstrate that our proposed solution outperforms existing state-of-the-art baselines.

Adaptive UAV Deployment for Remote IoT Computation Offloading in Integrated Space-Air-Ground Networks

Xiaomin Liu, Yujie Peng, Tiecheng Song, Southeast University; Xiaoqin Song, Nanjing University of Aeronautics and Astronautics