## 3 AoI Minimization in UAV-Assisted WSN/IoT Application Architectures
### Typical Architecture of UAV-Assisted WSN/IoT Applications  
Figure 5 shows a generic UAV-assisted WSN/IoT network where the UAVs fly to collect data from multiple sensor nodes that sense various environmental phenomena. The sensors could be deployed based on different distributions and orderings (random or deterministic). For instance, nodes can be deployed randomly within the sensed region. These nodes could form a clustered architecture, where a cluster head collects data from other sensor nodes and the UAV flies close to these nodes from the cluster head. In other cases, there could be stopping points that the UAV hovers above to collect data from neighboring sensors. The collected data can be sent to the BS or data center for further actions.  
In Figure 5, a UAV can hover above the sensors one after the other or hover above collection/stopping points to collect data before it finally offloads them to the BS or data center. Assuming the UAV hovers above each sensor, the AoI of the base station or data center initially starts at A\({}_{0}\) and increases at a unit rate until it receives updates from the UAV. Data are sampled by sensors at time \(\tau_{1}\) and received at \(\tau_{2}\) when the previous data at the BS are received. Thus, the AoI is \(\tau_{2}-\tau_{1}\) and the process continues [1]. In Figure 6, the AoI of each sensor is the elapsed time from when the information is sensed to when it is delivered to the data center [79]. Whenever the destination successfully collects the newest updated data, the AoI decreases. On the other hand, the AoI increases when the sensor nodes are outside the UAV's coverage area [92], which might be due to the UAV's limited coverage radius; wider radius results in a lower AoI because the UAV could collect data quickly from more sensor nodes [80]. Similarly, whenever the UAV has a greater communication range, it may communicate directly with more sensor nodes, collecting data faster while using less energy and thus reducing AoI. Consequently, it is crucial to  
Figure 5: A typical UAV-assisted sensor architecture where UAVs follow a trajectory to deliver fresh information to the data center.  
maximize the UAV's communication range [92]. The UAV's altitude also affects the AoI's performance because the propagation characteristics between IoT devices and the UAV, as well as the UAV and BS [8], affect the quality of communication.  
Classification of Multiple Design Aspects of AoI Minimization in UAV-Assisted WSN/IoT Applications in the Literature  
This section provides a detailed discussion of the identified primary and secondary design aspects shown in Figure 3. Note that the flight and trajectory as well as UAV and SN scheduling are identified as the most critical aspects for an efficient design of AoI-minimal UAV-assisted data-gathering WSN/IoT applications. In fact, all the reviewed papers considered UAV trajectory as the primary design objective, except the authors in [87]. In addition, it is also established from the existing literature that energy management and UAV altitude are fundamentally important in design considerations. Hence, the following subsections examine these fundamental design aspects with a summary provided in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref** & **Aspect** & **Summary** \\ \hline \multirow{3}{*}{[8]} & \multirow{3}{*}{Altitude scheduling} & Joint study of dynamic UAV altitude control and scheduling policy in a UAV-assisted wireless IoT network. \\ \cline{3-3}  & & Optimization of RIS configuration for AoI minimization in a network with UAV altitude constraints. \\ \hline \multirow{3}{*}{[9]} & \multirow{3}{*}{Altitude optimization} & AoI minimization in a scenario where UAVs return to the ground control station to be recharged. \\ \cline{3-3}  & & \\ \cline{3-3}  & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of design aspects’ classification for AoI minimization.  
Figure 6: Illustration of AoI evolution for a typical UAV-assisted WSN architecture.