## 2 Related Work
### Multiple Edge Servers  
Minimizing the mobile devices' energy consumption and reducing the mobile applications' execution latency in a multi-user system are the main goals of [22], in which the computational speed and the power of transmission for mobile devices are jointly optimized with the offloading ratio. Specifically, the authors formulated energy consumption and latency execution as non-convex problems. Then, they used variable substitution and univariate search techniques to obtain the optimal solution. Their results demonstrate that the proposed model can significantly decrease energy consumption and shorten latency with respect to the existing offloading schemes. A multi-user with a multi-edge offloading model for static and dynamic channels is proposed in [23]. First, on the static channel, the computation offloading decision is formulated as a non-cooperative potential game in which each mobile device can selfishly maximize CPU cycles and minimize consumed energy. On the dynamic channel (i.e., under time-varying and unknown channel state information), a Q-learning-based distributed computation offloading algorithm is formulated to determine the optimal solution, where the Nash equilibrium can be achieved. Finally, numerical results indicated that not only can this model outperform the energy consumption performance over local processing and random assignment, but it can also improve average long-term payoffs by up to 87.87% in comparison with the perfect channel state information case. [PERSON] et al. [28] proposed an energy-efficient offloading technique for multitask unloading of data based on UAV clusters for virtual and augmented reality games on vehicle networks. The VR/AR hybrid gaming scene should allow drivers and passengers to enjoy various visual effects in real time. However, due to dynamic changes in the real-time distribution of users, fixed edge nodes cannot cope with the changing load. Therefore, the use of UAVs together with fixed nodes as edge nodes helps to cope with the computing requirements of automotive networks. The deployment of a large number of UAVs is not practical due to mutual interference and the high cost of UAVs. To solve these problems, the paper presents a system with the following characteristics: multitask unloading (UAVs can work on several tasks and the results of one task can be used to perform other tasks in the future), UAV clusters working on one task, computing optimization, caching and communication resources, and AI-based decision making. In addition, an architecture for UAV clusters with various task implementations is proposed in which all the large scale applications for VR/AR games of vehicle networks are considered. Furthermore, an AI-based decision-making system is proposed to facilitate collaboration between UAVs and joint computing optimization, caching, and communication resources. The proposed architecture, compared with traditional MEC structures (both with and without UAVs), has advantages in the amount of information collected, real time performance, decision-making ability, and efficiency. Two algorithms were considered: preliminary deployment based on the analysis of historical data, and real-time deployment based on real-time perception. In addition, in this work, an experiment was conducted to predict the load of several UAVs based on long short-term memory (LSTM). Elsewhere, ref. [29] presented a multi-server task offloading model in a decentralized game manner with the goal of maximizing the system utility; a decentralized and efficient algorithm is proposed to derive the optimal offloading where the differential neural computing approaches and deep reinforcement learning models are combined without the need of prior knowledge of the user preference and network bandwidth. Meanwhile, in [30], unmanned ground vehicles (UGVs) are introduced as an effective solution to address the computing resources and battery limitations of UAVs, in which the intensive tasks can be offloaded from UAVs to be executed with UGVs.