Joint Trajectory and Scheduling Optimization for Age of Synchronization Minimization in UAV-Assisted Networks with Random Updates

Unmanned aerial vehicles (UAVs) are attractive in some Internet of Things (IoT) applications, due to their flexible deployment and extended coverage. In this paper, we consider an UAV-assisted network where the UAV flies between the resource-limited sensor nodes (SNs) and collects their status updates. The UAV trajectory and SN scheduling are jointly optimized to minimize the Age of Synchronization (AoS). In contrast to the conventional Age of Information (AoI), AoS takes into account both the freshness and the content of the information, which makes AoS a more suitable design criterion for information collection in an energy-constrained wireless network. Since the formulated problem is challenging to solve due to its non convexity, we reformulate the problem as a Markov decision process (MDP) and propose a deep reinforcement learning (DRL) algorithm to obtain the optimal solution with various action and state spaces. Our simulation results show the fast convergence rate of the proposed DRL algorithm and demonstrate that our proposed scheme can improve the performance of the UAV-assisted network compared to AoI-based schemes.