# Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing
## 1 Introduction  
As 5G networks and IoT develop rapidly, countless promising applications and services are emerging, including a High Definition (HD) livestream, autonomous driving, industrial automation and virtual reality, which will take advantage of the benefits that 5G networks will offer, including extremely high data rates, reduced latency, enhanced reliability and large-scale connectivity [1]. The number of IoT connection types is expected to reach 25 billion by 2025. However, the computational power of some IoT devices struggle to handle the large number of tasks due to their limited resources. The emergence of mobile edge computing provides strong support for task offloading and execution. In traditional edge computing, servers are usually deployed on terrestrial infrastructure communication facilities, which are susceptible to severe damage and loss of service capacity by natural disasters, such as earthquakes and landslides. In emergency scenarios,where finding safe routes and field rescue is critical, these tasks are often highly latency sensitive and computationally intensive [2], requiring offloading to resource-rich locations for processing. Limited by transmission distance, bandwidth and energy, as well as the possibility of the surrounding infrastructure often being damaged in a disaster, offloading tasks to surrounding areas for execution is not always feasible. Therefore, for the reduction of energy consumption and delay, tasks are offloaded to edge servers, which have more computing power [3]. The solutions to these problems have been made possible by the fast growth of the LEO satellite network. In the last few years, great progress has been made in the study of task offloading for LEO satellite networks. [PERSON] et al. investigated a task offloading scheme for LEO satellites to minimise task processing time by jointly optimising offloading decisions for IoT devices [4; 5; 6]. For the task offloading problem in SAGIN-based edge cloud computing systems, [PERSON] et al. consumed energy while adhering to the task's maximum latency constraint [7]. However, in emergency situations, none of the existing satellite task offloading efforts consider the inability of IoT devices to communicate directly with satellites for task offloading due to the disruption of the ground network infrastructure, which is limited by power. In this paper, we wish to address these issues. First, UAVs, with their high mobility and flexible deployment, can act as aerial base stations [8; 9; 10]. We propose a satellite-UAV-IoT device network architecture where multiple LEO satellites collaborate to offload computational tasks. The UAV acts as a connecting device between LEO satellites and IoT devices, observing satellite resource information and device task information to unload ground tasks to LEO satellites. Next, the problem is modelled as a constrained optimisation non-deterministic Polynomial (NP)--hard problem considering the task offloading of IoT devices and resource allocation of LEO satellites. Then, this optimisation issue is described as a Markov decision process (MDP), and a Deep Deterministic Policy Gradient and Long short-term memory (DDPG-LSTM)-based task offloading and resource allocation algorithm is designed to tackle the issue. Finally, an experimental environment for the simulation of the algorithm was created, and the findings demonstrate that the suggested approach saves the Weighting summation of the energy and latency by an average of 64.5% compared to the benchmark algorithm. The key contributions are the following.