## 3 AoI Minimization in UAV-Assisted WSN/IoT Application Architectures
### UAV Trajectory
It is important to carefully study UAV trajectory since packet transmission rate is highly dependent on the UAV's flight time between sensor nodes and its flight order over all the sensor nodes. Considering the importance of time effectiveness, the authors in [83] investigated the importance of AoI metrics in UAV-aided information sensing for environmental monitoring applications. The main objective was to study the UAV trajectory design and reduce packet expiry or dropped messages when a UAV is dispatched to collect data from a cluster of sensors. Data stored on sensor nodes are continuously generated and characterized by a finite lifetime. A reinforcement learning (RL) method was deployed to help the UAV learn how to adapt to the dynamic environmental conditions within the proposed framework to improve the time effectiveness and performance of path design. However, the waiting time of update packets [105], the scheduling policy of update packets, and queuing was not considered despite the fact that, in real applications, continuous sampling would require some packets to be queued. Although more complex, the potential of full-duplex radios was not explored despite their potential to facilitate high-bandwidth applications. Similarly, the authors assume the UAV must visit all sensor nodes before returning to the base station, which might not be optimal in large-sized networks [87]. A discrete trajectory was considered, which is prone to approximation errors as compared with real-world implementations [4]. The authors of [3] study the age-optimal data collection problem in UAV-assisted IoT (Figure 7a) by jointly considering the data sampling, queuing, and UAV-assisted relaying processes. Using a sample and replace policy, the authors considered a model involving the replacement of update packets in the sensor node buffer with those of a newly sampled packet. The UAV collects the sensor node update packets when it flies over the sensor nodes. In order to obtain the age-optimal policy, they formulate the UAV-assisted data collection problem as a finite horizon MDP intending to minimize the weighted sum AoI, packet drop rate, and energy consumption of the UAV. To overcome the curse of dimensionality, the authors propose using a deep RL algorithm for designing the UAV age-optimal trajectory while also minimizing the packet drop rate. Via simulations, they show that the proposed learning algorithm has the potential to reduce both the packet drop rate and AoI significantly using the learned topology and sensor node sampling status. However, the paper only considers a single-UAV scenario, and that of multiple UAVs has not been studied. A number of such UAVs can be investigated in the future to embark on data collection and further optimize the AoI collaboratively. Trajectory optimization and UAV-IoTD association are important design considerations in planning UAV-assisted WSN and IoT applications for data gathering. This is especially important when the set of IoT devices within the UAV coverage area varies with time. The authors of [2] 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 specific threshold) as shown in Figure 8a. They identify and formulate the associated AoI-minimized trajectory planning problems and propose a novel online AoI-based trajectory planning algorithm using deep RL considering the time-varying and unknown traffic generation patterns of IoT devices. To prevent being trapped in a local optimum and speed up the proposed algorithm's convergence, the authors adopt a randomized policy for pre-training deep neural networks. Via extensive simulations, the results show that the proposed algorithm can reduce the AoI of collected data.