## 3 Key Technologies of UAV-Assisted SAGINs
### Resource Management
#### 3.4.1 Task Scheduling  
The aerial network formed by UAVs has the following characteristics: (1) highly dynamic nodes and (2) highly dynamic network topologies. UAVs are often in motion, causing the network topology to change continuously due to the joining or leaving of nodes. Besides serving as communication relays, UAVs can also carry sensors and data collection equipment to obtain environmental parameters, optimizing user scheduling and network structure. Furthermore, the deep integration of SAGINs and edge computing has given rise to new edge cloud computing visions. In the following subsections, we will summarize task scheduling methods in UAV-assisted SAGINs from the perspectives of task types and application scenarios. 1. _Computation Offloading_: UAVs provide computational resources for terminal devices with low computational power (e.g., IoT devices) or offload tasks to ground base stations, edge servers, or satellite nodes, alleviating local computational loads and enhancing network computing performance. The flexible deployment of UAVs showcases their advantages in extending computational capacity. For instance, [PERSON] et al. [100] studied the UAV placement optimization problem, combining user association, partial offloading control, computational resources, and bandwidth allocation to reduce the weighted energy consumption of ground users (GUs) during computation offloading with UAVs. Since computation offloading optimization problems are typically difficult to solve and highly complex, many AI-based methods have been employed in recent years to jointly optimize computation offloading and resource allocation. For example, [PERSON] et al. [101] proposed a dynamic policy-based online optimization method using DRL and perception assistance to address uncertainties in UAV deployment and task arrivals, optimizing computational loads in SAGINs. 2. _Time Consumption_: Time complexity is a crucial metric for task scheduling, reflecting the efficiency of scheduling algorithms. Industries such as the industrial IoT and smart healthcare, which are highly latency-sensitive, often demand ultra-low latency. For latency-sensitive applications in the IoT, [PERSON] et al. [102] proposed a robust task scheduling algorithm with risk aversion, addressing a two-stage stochastic optimization problem to minimize latency. In a computation offloading scenario, [PERSON] et al. [103] designed an online offloading decision method based on UAVs flying along predetermined trajectories to collect IoT device tasks within their coverage. However, in reality, UAV flight trajectories are often not entirely fixed. Therefore, [PERSON] et al. [104] designed a task scheduling algorithm allowing UAVs to fly within a certain coverage area, employing a joint-optimization DRL method. Additionally, [PERSON] et al. [105] designed a task scheduling strategy that minimizes offloading and computation latency for all tasks under UAV energy constraints using a risk-aware reinforcement learning algorithm, identifying optimal parameters to balance latency and risk minimization. 3. _Environment-Oriented Algorithms_ : UAVs are known to be susceptible to environmental interference and impacts. For instance, network stability is poorer in disaster-affected areas compared to normal regions. Some studies focus on the environmental effects on task scheduling, such as how interference significantly affects the reliability of communication links among UAVs, BSs, and GUs. Regarding the joint optimization of energy saving and fair resource scheduling, [PERSON] et al. [68] proposed a DRL-based TD3 algorithm to optimize UAV trajectories, enhancing fairness between GUs and ground elements to resist interference. In emergency communication scenarios, [PERSON] et al. [106] studied the balance between task latency and energy consumption, proposing a method to minimize multi-UAV deployment costs. [PERSON] et al. [29] formulated the task scheduling problem in disaster scenarios as a two-stage Lyapunov optimization problem.