## 3 AoI Minimization in UAV-Assisted WSN/IoT Application Architectures
### UAV Trajectory
#### 4.1.3 Trajectory and Scheduling  
This category mainly consists of two papers. In the first paper, the authors consider joint trajectory and scheduling policy design in UAV-assisted sensor-based networks for age minimization. In the second, the authors investigate the UAV trajectory planning for maintaining information freshness in the IoT with unknown traffic generation patterns (such as the AoI evolution of a fire alarm triggered when the temperature is above a threshold), which differs from a regular periodic pattern. Further details on these works are provided below. The authors of [4] present a novel technique combining convex optimization and ML tools for optimizing the UAV flight trajectory and scheduling ground nodes' status updates. The aim was to minimize the UAV's normalized weighted sum AoI values. A UAV moves to ground nodes to collect status update packets about the processes observed by the sensor nodes. The problem for this setting is formulated as an NWAoI minimization problem, in which the UAV flight trajectory and packet update scheduling are jointly optimized. The authors draw many key insights from their analysis and analytically obtain a lower bound on the NWAoI, which is useful for determining nodes' weights. They show that the lower bound on NWAoI is independent of the number of nodes, but is rather a function of node weights and status updates. The authors show from the derived analytical expression that to make each node have a similar effect on NWAoI, the nodes' weights should be proportional to the total number of updates transmitted by the nodes. However, the authors only considered opportunistic transmission, thus, the scenario, where some packets might have to be buffered, was not considered. Consequently, the waiting time of update packets was not captured [105]. Moreover, the potential of backscatter communications was not studied [106]. However, the paper only accounts for one trip for the UAV, and a non-linear AoI function cannot be accommodated [87]. The authors of [81] consider a wireless network where several ground nodes are deployed for observing different physical processes for a geographic region. The UAV comes to collect status update packets while it seeks to maintain the freshness of its information status about the processes observed during its time of operation. They propose a novel RL framework to optimize the UAV trajectory and schedule status update packets to minimize the weighted sum AoI. A weighted sum AoI minimization problem is formulated where the joint optimization of UAV flight trajectory and scheduling of update packet transmission is considered. The problem is modeled as a finite-horizon MDP having finite state and action spaces, and the age-optimal policy was derived. However, the extreme curse of dimensionality in the state space makes it impractical to use a finite-horizon dynamic programming algorithm and rather a deep RL algorithm is proposed to tackle this problem. The authors prove the convergence of the proposed algorithm and numerically show its superiority over distance-based and random walk policies with respect to the achievable sum AoI per process. The authors were the first to apply deep RL tools to characterize the age-optimal policy. To avoid local optimum and speed up the convergence of the proposed algorithm, the authors adopted the randomized policy for pre-training deep neural networks. They show that the proposed algorithm can reduce the AoI of collected data via extensive simulations. However, the waiting time for update packets was not considered, since the authors studied a generate-at-will policy [105]. The paper also considered UAV trajectory with a single trip and not multiple trips [87]. Furthermore, discrete trajectory and time instants were studied, which might not fully capture the implementation in field environments due to approximation errors [4]. The UAV energy consumption was also not captured as discussed in [80], and the proposed solution was only near-optimal [106].