A Learning-Based Iterative Algorithm for AoI-Optimal Trajectory Planning in UAV-Assisted IoT Networks

In this paper, we employ an unmanned aerial vehicle (UAV) to ensurethe 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.