A Learning-Based Iterative Algorithm for AoI-Optimal Trajectory Planning in UAV-Assisted IoT Networks
Huang, Zihao Chen, Hai Gu, Bo Gong, Shimin Su, Zhou Guizani, Mohsen
Huang, Zihao
Chen, Hai
Gu, Bo
Gong, Shimin
Su, Zhou
Guizani, Mohsen
Supervisor
Department
Machine Learning
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Journal Article
Date
2025
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Language
English
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Abstract
In this paper, we employ an unmanned aerial vehicle (UAV) to ensure the freshness of sensing data, as measured by the age of information (AoI), in Internet of Things (IoT) networks. Specifically, the UAV switches between flying and hovering modes to collect data from widely distributed IoT devices. UAV trajectory planning, which determines the times and moments of data collection, is vital for optimizing the system AoI. Considering the limited UAV onboard energy and mission duration, AoI-optimal trajectory planning is formulated as a mixed-integer nonlinear programming (MINLP) problem. We first decompose the MINLP problem into two subproblems: a UAV time scheduling subproblem and a UAV path planning subproblem. Then, we propose a learning-based iterative (LBI) algorithm that consists of two modules: a successive convex approximation (SCA)-based module for solving the time scheduling subproblem, and a hierarchical asynchronous advantage actor-critic (A3C) module for addressing the path planning subproblem. The numerical results verify that the proposed LBI algorithm outperforms typical baselines in terms of the AoI performance.
Citation
Z. Huang, H. Chen, B. Gu, S. Gong, Z. Su and M. Guizani, "A Learning-Based Iterative Algorithm for AoI-Optimal Trajectory Planning in UAV-Assisted IoT Networks," in IEEE Transactions on Wireless Communications, doi: 10.1109/TWC.2025.3543042.
Source
IEEE Transactions on Wireless Communications
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Keywords
Autonomous Aerial Vehicles, Trajectory Planning, Internet Of Things, Trajectory, Optimization, Heuristic Algorithms, Data Collection, Wireless Communication, Performance Evaluation, Minimization
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IEEE