A Novel Sustainable AIoT Scheme for AAV-Assisted Communication Enabled by Radar Point Clouds and Moving Interaction Station

Autonomous aerial vehicle (AAVs) are increasingly utilized in smart city applications, such as Artificial Intelligence of Things (AIoT) systems due to their agility, low cost, and independence from ground road conditions. However, they require substantial computing resources and electricity consumption as support, leading to significant carbon emissions and energy consumption. This article presents a novel sustainable scheme for AAV-assisted low-altitude communication enabled by radar point clouds (RPCs) and the moving interaction station (MIS) to address this challenge. Vehicles are represented as rigid shapes with the 4-D RPC, which is close to the real world, and AAVs can ride on buses defined as MIS to conserve energy. Then, a joint optimization of the AAV trajectory, vehicle association, and resource allocation problem is formulated to complete the communication task and minimize energy consumption, which is limited by the Age of Information (AoI) indicators. An advanced hierarchical deep reinforcement learning (HDRL) algorithm containing a central control module and five submodules is proposed to solve the proposed optimization problem. The five submodules optimize the AAV trajectory, vehicle association, and resource allocation to complete the communication task with the constraint of AoI indicators. The central control module manages five submodules to minimize energy consumption by scheduling time. Simulation results demonstrate that the proposed scheme is effective for smart city applications, and the HDRL algorithm achieves competitive performance compared with the benchmarks.