Time-efficient approximate trajectory planning for AoI-centered multi-UAV IoT networks

Abstract

The gathering of data produced by ground Internet of Things (IoT) devices can be facilitated with the assistance from Unmanned Aerial Vehicles (UAVs) especially in hard-to-reach areas. However, the limited battery of UAVs requires a careful planning of their trajectories. As the timely delivery of data can be critical in certain applications, Age of Information (AoI) should also be integrated during this planning. Most of the existing works that study AoI-centered UAV trajectory planning focus on the timing of the data gathering by the UAV, without considering the time UAV needs to deliver it to a specific point. This study broadens the perspective by incorporating multiple UAVs and Ground Base Stations (GBSs) throughout the region, to be used for the delivery of data collected by UAVs, defining the AoI. We also allow UAVs to visit IoT locations only after a data is generated, which can happen during the mission of UAVs. Our goal is to optimize the UAV trajectories considering multiple prioritized goals, namely, minimization of maximum AoI, then the minimization of sum of AoI for all collected data and finally the sum of UAV path lengths. Using Integer Linear Programming (ILP), we first find out the optimal solution. In order to avoid the long running times and provide a scalable yet time-efficient solution, we propose a heuristic based method. Extensive simulation results under various setups show that the heuristic approach provides results with reasonable margins to ILP results and is also scalable, making the proposed solution more practical.