Fly, Sense, Compress, and Transmit: Satellite-Aided Airborne Secure Data Acquisition in Harsh Remote Area for Intelligent Transportations

Satellite-aided airborne systems can enable data acquisition in remote areas for the intelligent transportation systems (ITS), leveraging the satellite coverage alongside the mobility and multifunctional capabilities of autonomous aerial vehicles (AAVs). However, due to the harsh environment, ensuring secure and timely task execution is complicated by uncertainties related to both channel conditions and eavesdropping threats. This paper proposes a two-stage optimization method to fully exploit AAVs' flying, sensing, compressing, and transmitting capabilities for secure data acquisition under dual uncertainties. In the first stage, a deep reinforcement learning strategy is employed to optimize sensing and trajectory planning to explore the eavesdropping environment and balance computational and transmission demands. Building on the sensed information about the eavesdropping environment, the second stage focuses on minimizing task completion time through optimal resource allocation and hierarchical A∗ path planning, with the channel uncertainty addressed by incorporating an outage probability constraint. Simulations demonstrate that the proposed method can reduce data completion time by 35.3%, validating its effectiveness in uncertain environments.