AoI-optimal path planning for UAV-assisted data collection with heterogeneous information aging speed

This article investigates a problem involving the collection of data from sensor nodes (SNs) with heterogeneous information aging speed (IAS) by an unmanned aerial vehicle (UAV) in a dense obstacle environment. The objective is to minimize the average age of information (AoI) of the SNs through UAV path planning. The problem is challenging due to the tight coupling of obstacle avoidance, information timeliness, and the heterogeneity of SNs. Directly solving this path planning problem is difficult, and the conventional approach involves planning the access sequence without considering obstacle avoidance and then optimizing the UAV trajectory while incorporating safety constraints. However, optimizing the trajectory for safe flight introduces changes in the flight time cost, resulting in the average AoI not reaching its minimum value. To address this, a UAV safe flight network is first established by generating trajectories using a combination of A*-based and successive convex approximation (SCA)-based algorithms. Subsequently, a genetic algorithm (GA)-based method is employed and compared with the time greedy strategy. The numerical results demonstrate that the time greedy strategy, which aligns with intuitive understanding, can achieve a smaller total UAV flight time, while the proposed method effectively minimizes the average AoI of SNs.