## 3 System Model
### Network Model  
The proposed LEO satellite network includes \(N\) observation satellites, \(K\) relay satellites, \(M\) IoT devices, and a ground station. Observation satellites equipped with sensors and instruments to collect data from the Earth's surface, such as imagery or environmental measurements, focus on data acquisition and initial data processing [41, 42]. Relay satellites are defined as satellites that facilitate communication by receiving data from observation satellites or IoT devices [43, 44] and forwarding it to other satellites, ground stations, or processing nodes. They possess greater computational and communication capabilities to handle data relay and task offloading. In this paper, we distinguish between two types of information handled by satellites: data and tasks. Data refer to raw information transmitted or forwarded without additional processing, such as images relayed from an observation satellite to a ground station. Tasks, on the other hand, involve computational workloads where data need to be processed to extract useful insights. For example, an IoT sensor may collect temperature readings, which require analysis to detect anomalies. In our model, if data only require transmission, we classify it as 'data'; if computation is required, we classify it as a 'task'. When direct communication between an observation satellite and the ground station is not possible due to their relative positions, observation satellites gather data from the Earth's surface and transmit it to relay satellites, which then relay the data to the \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**References** & **Focus Area** & **Methodology** & **Key Contributions** & **Limitations** \\ \hline
[17, 18] & Task offloading with energy optimization & Game theory, energy-efficient algorithms & Proposes energy-efficient offloading strategies with service quality constraints & Does not address joint resource scheduling \\ \hline
[19, 20, 21] & Delay-optimized task offloading & Deep reinforcement learning & Reduces task execution delays through offloading optimization & Limited focus on computational resource scheduling \\ \hline
[22, 23, 24, 25] & Energy and delay optimization & Heuristic algorithms & Balances computational load and reduces system energy consumption and delays & Lacks adaptability to dynamic network conditions \\ \hline
[26, 27, 28, 29] & Satellite resource scheduling for communication & Optimization algorithms & Enhances inter-satellite bandwidth usage and throughput & Focuses only on data transmission scheduling, not computation \\ \hline
[29, 30, 31, 32, 33] & Resource scheduling in hybrid networks & Software-defined networking, Q-learning & Improves communication, storage, and computing efficiency in hybrid networks & Assumes static resource demands and lacks joint optimization \\ \hline
[34, 35, 36] & Joint task offloading and resource scheduling & Deep reinforcement learning, convex optimization & Proposes energy-efficient strategies for task segmentation and scheduling & Limited handling of dynamic network environments \\ \hline
[37, 38, 39, 40] & Long-term optimization in LEO networks & Deep reinforcement learning, joint optimization & Provides comprehensive strategies for delay and energy optimization in dynamic scenarios & Assumes fixed task arrival rates and limited scalability \\ \hline This work & Joint task offloading and dynamic resource scheduling & Deep Q-Network-based optimization & Long-term cost minimization, real-time adaptability to dynamic task arrivals and link variability & Requires high computational resources for reinforcement learning model training \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of related works on task offloading in LEO satellite networks. ground station cache. For example, the data can be raw or processed information, such as images, sensor readings, and measurements that need to be transmitted to ground stations.