## 3 AoI Minimization in UAV-Assisted WSN/IoT Application Architectures
### UAV Trajectory
#### 4.1.3 Trajectory and Scheduling
This category consists mainly of four works with different notions of energy management, as opposed to several other works that have considered both (but not strictly) energy management and trajectory optimization as will be observed later. The first of the four [5] requires the UAV to return to the ground control station to be recharged if at any time it has insufficient energy to complete its mission. In the latter [6], UAVs are considered to have different energy capabilities resulting in some heterogeneity. Similar to the first case, UAVs may either choose to perform the data acquisition task or return to the data center. They should ensure sufficient energy is available to return to the data center; however, they are not recharged. In [90], the authors were motivated by the necessity to charge UAVs while aiming at minimal information age. In [85], the authors consider a wireless-powered IoT network with radio frequency energy harvesting (RF-EH) where the UAVs transfer energy to sensor nodes and also collect data from them. These works are discussed in much more detail below. UAV trajectory optimization is crucial for data collection in energy-constrained IoT devices. Prior works to [5] have not considered the potential of UAV recharging despite the limited energy of the UAV. Thus, the authors incorporate UAV recharging in a post-disaster early warning UAV-assisted data collection scenario. In the studied model (Figure 10a), the authors assume that UAVs perform data collection in teams in a scenario with changing AoI requirements and UAV power levels. As opposed to prior works using MDP, the authors did not divide the environment into discrete areas with separate action spaces. The UAV moves toward ground nodes for collecting data and is also recharged. To ensure the charge of the UAV is sufficient and the data remain fresh, the authors formulate the path planning as a semi-MDP and use a proposed hierarchical DQN to handle this problem. This way, the agent can select options at a high level, which dictates its actions. Optimizing the AoI with heterogeneous UAVs using deep RL is studied in [6]. Particularly, this is motivated by the fact that the UAV has limitations in the service it can provide due to its small-sized antenna, low transmit power, and initial battery capacity. Prior works (see refs. in [6]) study single UAV scenarios and do not study the heterogeneous UAV swarm setting needed to meet the target requirements in delay-sensitive applications and for better service of timely data collection. In such cases, planning their trajectory becomes a great challenge. Thus, the authors study AoI optimization in the scenario with a heterogeneous UAV swarm where path planning is the main objective. Additionally, they propose using an encoder-decoder model using an attention-based RL algorithm designed for UAV swarm path planning. Simulation results show that the proposed algorithm converges quickly and is characterized by a high reliability and efficient optimization capability to solve the AoI-based heterogeneous UAV cooperative swarm optimization problem effectively. Although the authors consider free space path fading and additional losses due to obstruction, it would be interesting to study this proposal with data obtained from real measurements in future works. In [90], the authors consider a UAV constrained in mobility (modeled by a graph \(G\)) which gathers information from several ground terminals having lower power transmitters. The UAV moves close to the nodes when it collects information. Due to the short transmit-receive distance and static nature of the network, the authors assume that interference and path loss in such a system can be ignored. They also consider constraints involving charging rate and battery capacity (i.e., energy constraints). They first formulate the lower bound of peak age for the graph under energy constraints and the results prove that the low bound is highly influenced by the charging rate, while the battery capacity's influence on the low bound is limited (much lower).