# Offloading of Atomic Tasks in Satellite Networks:
## 2 Related Work
In order to solve the problem of the rapid increase of vehicles and the in-vehicle terminal can not achieve efficient calculation, [PERSON] et al. [20] proposes a task offloading strategy for an edge-computing architecture based on reinforcement learning to compute for vehicular networking. [PERSON] et al. [21] proposed task offloading algorithm effectively optimizes task latency and computational resource consumption in a multi-user and multi-server airborne edge computing scenario. [PERSON] et al. [22] proposes a mobile computing edge-based offload solution for Telematics tasks from a global perspective to minimize the average time to complete a task. [PERSON] et al. [23] achieves efficient unloading of vehicle tasks from a game-theoretic perspective and considering the time delay constraint of task unloading. [PERSON] et al. [24] proposed a mobile-aware partial task offloading algorithm to improve the utilization of vehicle computing resources. [PERSON] et al. [25] A introduces an emerging IoT architecture in the blockchain and implements algorithms for task offloading and resource allocation in the form of smart contracts in the blockchain, ultimately optimizing resource allocation in the IoT. [PERSON] et al. [26] verifies that task offloading optimization is a potential game problem and solves it with a distributed algorithm of Lagrange multipliers. [PERSON] et al. [27] considers the limited computational resources and battery capacity of existing mobile devices resulting in the inability to meet the demand of low computational power and latency, the authors use a potential game model to solve the distributed task offloading problem. [PERSON] et al. [28] proposes to describe task offloading as an integer for network load and transmission interference problems. The nonlinear programming problem is a task offloading algorithm based on a differential evolutionary algorithm. [PERSON] et al. [29] proposes an algorithm that considers completion time constraints and task dependency requirements and schedules all tasks from different applications in a priority queue. The aim is to protect low-energy mobile devices and keep them alive for as long as possible during the allocation process to establish an offloading policy. [PERSON] et al. [30] considers quantitative user experience as an optimization goal for offloading decisions and develops an efficiency-based offloading decision algorithm. [PERSON] et al. [31] proposes two computational offloading algorithms, namely binary offloading and partial offloading. Binary offloading offloads the task as a whole to the mobile edge computing server, and the server selects only the best offload location, thus reducing the total latency cost and energy consumption. [PERSON] et al. [32] proposes an efficient and low-complexity offloading scheme. A series of reconfigurations based on reconfiguration linearization techniques are performed and then programmed by further utilizing the product alternating direction method (ADMM) and the convex function difference method (DC). The authors propose a parallel optimization framework and obtain a more favorable TN with two orders of magnitude time complexity lower than the centralized optimization algorithm. [PERSON] et al. [33] investigates a potential game called multitasking pair offloading (POMT). [PERSON] et al. [34] proposes a two-step method that aims to satisfy the delay constraint while minimizing the energy consumption of IoT devices, ultimately reducing the offloading energy for a given set of delay-constrained offloading tasks. [PERSON] et al. [35] focuses on offloading tasks on a two-tier mobile edge computing environment to minimize the total energy consumed by users; [PERSON] et al. [PERSON] et al. [36] proposes an energy-efficient and deadline-aware task offloading strategy based on channel constraints. It is to minimize the energy consumption of mobile devices while satisfying the constraints of mobile cloud workflow; [PERSON] et al.