Learning-Based Multi-UAV Assisted Data Acquisition and Computation for Information Freshness in WPT Enabled Space-Air-Ground PIoT
Jiayan Liu, Xiongwen Zhao, Peng Qin, Suiyan Geng, Zhiyu Chen, Hongxi Zhou
Abstract
The explosion of information data in the power Internet of Things (PIoT) poses difficulties for data acquisition and computation of power devices located in remote areas with limited computing capability and energy. Therefore, we propose a multi-unmanned aerial vehicle (UAV) assisted wireless power transmission (WPT) enabled space-air-ground PIoT framework, in which the UAVs provide energy for data acquisition of power devices through WPT, and use mobile edge computing (MEC) technology to calculate the data. Finally, they act as relays to forward the data to the low earth orbit (LEO) satellite. For keeping the data fresh, we jointly optimize the number of UAV hovering positions, the hovering positions, the connection between UAVs and power devices, the energy transmission and data acquisition time of power devices, the computing resources, flight speed and trajectories of UAVs, aiming to minimizing the average age of information (AoI) of power devices. Through theoretical derivation the problem is decoupled into five independent subproblems, which are solved using improved K-means algorithm, Lagrangian dual decomposition method, interior point method, adaptive optimization, and Q-Learning algorithm, respectively. Extensive numerical simulations demonstrate the superior properties of our solution comparing to benchmark algorithms.