A Heuristic Approach to Minimize Age of Information for Wirelessly Charging Unmanned Aerial Vehicles in Unmanned Data Collection Systems

Zhengying Cai (), Yingjing Fang, Zeya Liu, Cancan He, Shulan Huang and Guoqiang Gong
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Zhengying Cai: Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipment, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China
Yingjing Fang: Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipment, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China
Zeya Liu: Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipment, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China
Cancan He: Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipment, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China
Shulan Huang: Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipment, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China
Guoqiang Gong: Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipment, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China

Mathematics, 2025, vol. 13, issue 21, 1-26

Abstract: Wirelessly charging unmanned aerial vehicles (WCUAVs) can complete charging tasks without human intervention and may help us efficiently collect various types of geographically dispersed data in unmanned data collection systems (UDCSs). However, the limited number of wireless charging stations and longer wireless charging times also pose challenges to minimizing the Age of Information (AoI). Here, we provide a heuristic method to minimize AoI for WCUAVs. Firstly, the problem of minimizing AoI is modeled as a trajectory optimization problem with nonlinear constraints involving n sensor nodes, a data center, and a limited number of wireless charging stations. Secondly, to solve this NP-hard problem, an improved artificial plant community (APC) approach is proposed, including a single-WCUAV architecture and a multi-WCUAV architecture. Thirdly, a benchmark test set is designed, and benchmark experiments are conducted. When the number of WCUAVs increased from 1 to 2, the total flight distance increased by 12.011% and the average AoI decreased by 45.674%. When the number of WCUAVs increased from 1 to 10, the total flight distance increased by 87.667% and the average AoI decreased by 78.641%. The experimental results show that the proposed APC algorithm can effectively solve AoI minimization challenges of WCUAVs and is superior to other baseline algorithms with a maximum improvement of 9.791% in average AoI. Due to its simple calculation and efficient solution, it is promising to deploy the APC algorithm on the edge computing platform of WCUAVs.

Keywords: unmanned data collection systems (UDCSs); Age of Information (AoI); wirelessly charging unmanned aerial vehicles (WCUAVs); autonomous aerial vehicles (AAVs); trajectory optimization problem; heuristic approach; artificial plant community (APC); edge computing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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