## 3 AoI Minimization in UAV-Assisted WSN/IoT Application Architectures
### UAV Trajectory  
The optimization of UAV trajectory is important to utilize the UAV effectively in view of the limited resources [62] and constrained battery capacity of the UAV. This section discusses papers that have mainly studied trajectory in system design and optimization. Four papers best fit this category: the first [79] can be categorized as _trajectory only_ as it presents the simplest case, while the other [83] mainly involves trajectory design and minimization of packet expiry. In the third [3], trajectory and sampling policies of IoT devices are considered, which adds some complexity to the system. Finally, UAV trajectory is considered with UAV-IoTD association in [2] due to the dynamic nature of the network where a different number of IoT nodes may be associated with the same UAV over time. The authors in [79] investigate the impact of AoI on UAV-assisted data collection. In the proposed work, the age-optimal trajectory planning problem is studied in UAV-assisted WSN where the UAV visits all sensor nodes in a sequential manner and delivers the data to the data center for information processing. The authors investigate the impact of UAV trajectory on the AoI of each sensor node where the AoI of each node corresponds to the time taken for information to get from the sensing point to the data center. However, this work did not examine the joint optimization of the data acquisition method and visiting node order [93]. Similarly, ref. [95] did not investigate the use of full-duplex devices, instead, half-duplex UAV transmissions were studied, and only uplink transmissions were \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref** & **Aspect** & **Summary** \\ \hline
[6] & Trajectory and energy management & AoI optimization with UAVs possessing varying energy capacities which introduces some degree of heterogeneity. \\ \hline
[84,89] & SN association & Design of sensor node association and trajectory planning for UAV-assisted data collection. \\ \hline
[4,81] & Trajectory and scheduling & Optimize the flight path of a UAV and schedule updates of the ground nodes’status. \\ \hline
[2] & UAV-IoTD association & The UAV trajectory planning for maintaining information freshness in IoT. \\ \hline
[3,105] & Node sampling policy & The UAV trajectory planning for maintaining information freshness in IoT. \\ \hline
[87] & Scheduling and energy management & Age-optimal UAV scheduling with battery recharging. \\ \hline
[105] & Sampling policy & Trajectory design of UAV was created before the UAV’s flight and not in real-time. \\ \hline
[7,80,82,92] & Scheduling & Ground node transmissions of status update packets are scheduled \\ \hline
[86] & Mode selection & Framework where a battery-limited UAV flies in multiple turns. \\ \hline
[1] & Bandwidth allocation & UAV-assisted energy-aware data collection for a group of IoT devices. \\ \hline
[106] & Data collection time & Joint optimization of UAV trajectory, transmit power, and data collection time from each sensor on the backscatter IoT architecture \\ \hline
[91] & SN-CP association & Cooperative framework for energy-constrained multi-UAV data collection for time-sensitive WSNs. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._investigated. Additionally, before returning to the base station, the UAV visits all the sensor nodes, which might not be optimal with respect to AoI [87]. The unreliable channel conditions and the absence of knowledge of channel state information (CSI) were also not considered [8]. Furthermore, the studied architecture assumed a fixed energy supply for sensor nodes, making it difficult to deploy in dangerous (or hard-to-reach) areas where it cannot be recharged [85]. The concept of UAV recharging was not explored [90], and only one UAV was considered, which makes it unsuitable for large-scale sensor networks [91]. Finally, only a near-optimal (as opposed to an optimal) solution was derived [106], and the optimal scheduling of update packet transmissions was not considered [81].