AoI-Minimal Task Assignment and Trajectory Optimization in Multi-UAV-Assisted Wireless Powered IoT Networks

• Published in: Drones (Basel) Vol. 9; no. 2; p. 90
• Main Authors: Gu, Yu, Qiu, Hongbing, Chen, Baoqing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2025
• Subjects: age of information (AoI); Airspeed; Algorithms; Associations; Cluster analysis; Clustering; Communication; Data collection; Data transmission; Devices; Energy consumption; Energy harvesting; Energy storage; Energy transfer; energy transfer and data collection; Genetic algorithms; Hovering; Internet of Things; internet of things (IoT); Kuhn-Tucker method; Localization; Optimization; task assignment and trajectory optimization; Trajectory optimization; Trajectory planning; unmanned aerial vehicle (UAV); Unmanned aerial vehicles; Vector quantization; Wireless networks
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones9020090

This paper investigates the energy transfer and data collection problem of multiple unmanned aerial vehicle (UAV)-assisted wireless-powered Internet of Things (IoT) networks. To ensure information freshness for IoT devices and reduce UAVs’ energy consumption, we minimize the average Age of Information (AoI) of IoT devices by jointly optimizing the energy harvesting (EH) and data collection time for IoT devices, the selection of data collection points (DCPs), DCP-IoT associations, and task assignment, flight speed, and trajectories of UAVs, subject to the limited endurance of UAVs. As this problem is nonconvex, we propose a novel DCP association and trajectory-planning scheme that seeks age-optimal solutions through an iterative three-step process. First, we calculate the EH and data collection time for IoT devices using Karush–Kuhn–Tucker (KKT) conditions. Then, we introduce an optimal hovering time allocation-based affinity propagation (OHTAP) clustering algorithm to determine optimal DCP locations and establish DCP-IoT associations. Finally, we develop two algorithms to optimize UAVs’ trajectories: an improved partheno-genetic algorithm with enhancement mechanisms (EIPGA) and a hybrid algorithm that combines improved MinMax k-means clustering with EIPGA. Numerical results confirm that our scheme consistently outperforms benchmark schemes in AoI performance and solution stability across diverse scenarios.