Age of Information Minimization in QoS-Aware UAV-assisted Wireless-Powered Data Collection

Unmanned Aerial Vehicles (UAVs) are pivotal in the real-time and autonomous data collection for the Industrial Internet of Things (IIoT) through wireless sensor networks (WSNs). To ensure the data’s freshness, known as the Age of Information (AoI), we propose an innovative approach to optimize the utility design of limited resources and UAV trajectory. Our research is driven by minimizing the average AoI by optimizing the UAVs’ trajectories and time allocations per energy-harvesting ground node (GN) while meeting their quality of service (QoS) demands regarding the system or sum throughput. We have developed a novel control-communication cross-domain resource allocation strategy that optimizes the interplay between control Key Performance Indicator (KPI) AoI and communication KPI sum throughput to ensure seamless data collection in wireless-powered IIoT. Initially, we formulated a non-convex optimization problem for minimizing the average AoI in a wireless-powered data collection network while complying with the constraints on total throughput and operation time. We then decomposed this challenging problem into two sub-problems: time allocation and trajectory planning. We addressed the former problem using commercial convex optimization tools while we designed the UAV’s trajectory by exploiting a genetic algorithm (GA)-based framework. Our simulations, conducted under various scenarios, have successfully validated the performance of our proposed joint optimization methodology. They showcased a significant reduction of up to 80% in average AoI against the benchmarks, providing robust evidence of the effectiveness of our approach.