## 3 AoI Minimization in UAV-Assisted WSN/IoT Application Architectures
### Trajectory and Energy Management (TE)
#### 4.2.5 TE and Scheduling  
This section will discuss several works that have considered UAV trajectory design, energy management, and scheduling. Depending on the architectural setup and underlying assumptions, scheduling can take several forms. In [1], the authors consider bandwidth allocation and scheduling of IoT devices for transmission in an unequal time-slotted system where the UAV hovers above them. In the second work, the service time is allocated for IoT devices in the uplink and UAV in the downlink, respectively. In [80], the authors assume that UAVs fly toward the sensors to collect data within a particular duration and optimize the scheduling of sensor nodes to minimize the weighted average AoI of all sensor nodes. In [7], the authors emphasize the importance of hovering points and the visiting order of sensor nodes on AoI as they greatly influence the UAV flight time. The authors in [92] focus on handling the sensor node transmission opportunities in a time-slotted system where sensor nodes sample data packets using a sampling strategy and the sampled data are stored in a buffer. The joint optimization of power consumption and age performance is important to improve UAV-assisted data collection efficiency and quality in such scenarios. Optimizing the transmission energy of IoT devices and proper bandwidth allocation saves more energy which can significantly prolong the sensor lifetime. The authors in [1] investigated UAV-assisted energy-aware data collection for a group of IoT devices deployed in a geographical area to minimize the weighted sum of expected AoI, propulsion energy consumption, and transmission energy consumption of IoT devices. In the considered architecture (illustrated in Figure 12), IoT devices are assumed to upload data continually and a UAV helps to transfer the data to the base station. Scheduled IoT devices upload the sensed data to the UAV while it is hovering and the UAV relays these data to the base station before it flies to the next hovering location. The proposed scheme can decrease the expected AoI and energy consumption, thus motivating the need to jointly optimize the UAV trajectory, IoT device scheduling, and channel allocation for enhancing UAV-assisted data collection performance. The authors of [82] consider an IoT-based setup with a source-destination pair where the UAV acts as a relay. In this setup, the source node sends its measurements (status updates) to the destination node. The source node could be an IoT gateway close to IoT devices that transmits measurements to the destination (e.g., base station) via the UAV. They formulated the average peak AoI minimization problem to jointly optimize the UAV flight trajectory, energy allocation, and service time duration for the transmission  
Figure 12: Illustrative architecture for UAV-based data gathering in heterogeneous IoT based on [1]. of update packets at the source node and UAV. The formulated problem is non-convex, thus an efficient iterative algorithm is proposed to solve the problem, and the algorithm's convergence is analytically established. In order to achieve this for a given UAV flight trajectory, the authors characterized the optimal energy and the service time allocation for packet transmission. This subproblem is particularly interesting when the UAV's trajectory cannot be altered considering its energy and other mission-critical constraints. These results derived show several insights and closed-form characterization of some of the subproblems captured. For instance, the results emphasize the importance of optimizing the UAV's flight trajectory to ensure the freshness of collected status updates at the destination, particularly when the source node's available energy is limited, or update packets are large in size. However, the UAV cannot make real-time decisions when its trajectory is designed before its flight [105]. Additionally, the joint optimization of the visiting order of the node and the mode of data acquisition (as studied in [93]) was not considered.