## 3 AoI Minimization in UAV-Assisted WSN/IoT Application Architectures
### UAV Trajectory
#### 4.1.1 Trajectory and Sensor Node Association  
The consideration of AoI in network design could lead to advancements in the proposal of novel transmission and scheduling policies which are essential in UAV-enabled applications. In large-sized networks with several CPs, the sensor nodes have to be associated with CPs based on a policy. Thus, new ML-based techniques have recently been deployed for network optimization. For instance, the authors in [89] focus on the design of sensor node association and trajectory planning for UAV-assisted data collection to minimize sensor node maximal AoI. In [84], the focus was on designing an AoI-aware UAV-assisted data collection strategy in a scenario where the UAV hovers above each CP to collect data from a set of sensor nodes (Figure 9a). To minimize the maximum AoI of all sensor nodes, a joint optimization of CP association and UAV flight trajectory was carried out via a joint sensor nodes association and trajectory planning policy. The location of CPs was first determined via an AP-based algorithm, which was also used to determine the association between sensor nodes and CPs. Dynamic programming was then used to  
Figure 8: Illustrative architecture for UAV-assisted IoT without cellular coverage (**a**) and EH-UAV-assisted IoT (**b**) based on [2; 92] respectively.  
Figure 7: Illustrative architecture for single- (**a**) and multi- (**b**) UAV-assisted data collection from IoT with queuing policy based on [3; 105] respectively.  
obtain an age-optimal trajectory along CPs by iterating these two steps alternatively. Via simulations, the authors show the effectiveness of the proposed method above.  
The age-optimal UAV-assisted data collection problem is extended and generalized in [89]. In this case, the rotary wing UAV is dispatched from the data center to collect data from ground sensor nodes, which are grouped into (non-overlapping) clusters. A data collection point is associated with each cluster. The UAV flies from one CP to the next to collect data from the associated sensor nodes following a trajectory. In this framework, the focus was to jointly optimize the locations of the CPs, sensor node-CP association, sensor nodes' uploading sequence, and the trajectory of the UAV along the CPs for minimizing the sensor node maximum and average AoI values. Using two iterative steps involving an affinity-propagation-based association algorithm with clustering weight, the authors found the locations of CPs and the association between the sensor node and CP. The authors show the effectiveness of the proposed strategy via simulations, particularly the proposed algorithm, which can balance sensor nodes' uploading time and UAV flight time to minimize the sensor node maximum or average AoI in various scenarios. However, the online decision-making paradigm where the UAV learns its next move on the fly was not considered. In other words, UAV flight is designed before the UAV leaves to collect data, which does not facilitate the real-time decision-making of the UAV [105]. Similarly, the impact of the energy limitation of low-power IoT devices and the dynamic nature of the time-varying channel was not studied even though channel attenuation impacts transmission efficiency [102].