# Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing
## 2 Related Work  
Task offloading supported by LEO satellite edge computing is a promising method in dealing with energy and computationally resource-constrained IoT devices effectively. In prior works on task offloading, to reduce the consumption of energy or latency of ground-based devices, [PERSON] et al. studied the offloading problem in multiple IoT device scenarios and proposed a strategy for the allocation of resources and offloading, which significantly reduced the average system cost [11; 12]. [PERSON] et al. introduces a multi-stage offloading scheme to obtain the most appropriate offloading strategy and reduce the average request response latency and request cost [13]. [PERSON] et al. propose a strategy considering the differences in terminal tasks and computing capabilities, energy and latency minimisation [14; 15]. In studying hybrid task offload, [16; 17] proposed hybrid cloud and edge computing LEO satellite networks with a three-tier computing architecture, jointly considering cooperation between mobile users, LEO satellites and cloud servers. [PERSON] et al. considered cooperative offloading between LEO satellite networks, jointly optimising offloading and resource allocation strategies aiming to minimise the weighted sum of user latency and energy consumption.Tang et al. considered the LEO coverage time and computing power to minimize the energy consumption of users by optimizing offloading decisions. [18] investigated cooperative offloading strategies for satellite edge computing systems and terrestrial base stations, considering satellite orbit characteristics and optimising offloading strategies to reduce energy consumption and latency. To make full use of the computing power of cloud servers, [PERSON] et al. proposed an LEO-assisted terrestrial satellite network architecture for collaborative computing offloading at the cloud edge to minimise system energy consumption within the constraints of latency and other Quality of Service (QoS) requirements [19]. The focus of some work has been on UAV-assisted task offloading, where mobile and flexible UAVs can enhance task offloading efficiency. To maximise computational efficiency and task queue stability, [PERSON] et al. propose a DRL-based scheme to optimise offloading and resource allocation [20; 21]. Considering the limited computational resources of UAVs, [PERSON] et al. propose a strategy for offloading to ground-based base station servers to optimise transmit power and base station selection under the practical constraints of task completion latency and power consumption [22]. In a UAV-enabled mobile edge computing system based on device-to-device communication, the overall energy efficiency is maximised by optimising UAV and node transmit power and scheduling strategies in order to improve the balance between different types of nodes [23]. For channel uncertainty during offloading, [24] minimises the energy consumption under constraints such as the user quality of service by optimising the CPU frequency and user transmit power, etc. To effectively support the communication and computation of unmanned surface vehicles, [25] jointly considered the UAV flight speed and offloading decision to minimise the energy consumption of the UAV swarm under the condition of ensuring the time delay constraint. [PERSON] et al. considered the energy limitation problem of UAVs and proposed the strategy of offloading the mission to the ground base station by optimising the communication scheduling and resource allocation, etc., considering the location relationship of the ground base station and energy efficiency, and aimed to minimise the total energy consumption [26]. Several efforts focused on joint UAV and LEO satellite processing task offloads. [PERSON] et al. consider allocating computing and communication resources to build a resource allocation and task scheduling system. UAVs are responsible for collection tasks, and satellites provide edge computing services.