# Vehicular Edge-Computing Framework for Making Use of Parking and Charging Electric Vehicles
## 2 Related Work  
Vehicular edge computing can provide efficient computing services for vehicular applications. However, the edge resources are limited, and we should consider the optimal utilities of the computing resources, which has been a hot research topic and extensively studied by many scholars. Considering the interests of both the service requester and provider, [PERSON] et al. [6] presented a dual-end optimization problem that tried to balance the interests of the user side and the server side. On the user side, it is dedicated to optimizing the offloading strategy and the frequency of the local central processor. On the server side, the main focus is on optimizing resource scheduling and service provisioning. Considering vehicular edge computing assisted by solar RSUs, [PERSON] et al. [7] proposed a heuristic algorithm based on dynamic programming, which can jointly perform task partitioning and offloading in real-time, as well as accomplish adaptive decisions at the system and application levels, thus reducing end-to-end latency. However, in the above research works, it is supposed that the edge computing resources are sufficient, which is not the truth in actual situations. [PERSON] et al. [8] proposed a machine learning-based, region-/context-aware equipped content pre-caching strategy that addresses the problem of pre-caching of early decisions and predicted results. Some scholars have used optimization methods to find the optimal strategy for computation offloading. [PERSON] et al. [9] proposed a particle swarm optimization-based approach to minimize the energy and resource consumption in computation offloading with the delay constraint. [PERSON] et al. [10] considered the long-term scheduling policy optimization, and modeled the problem as a specific Markov decision process (MDP) based on the scheduling queue. Then, a deep reinforcement learning (DRL)-based algorithm was proposed to find the solution. [PERSON] et al. [11] developed an efficient task = scheduling algorithm, and the basic idea is to prioritize multiple applications and tasks to ensure the completion time constraints of the applications and the processing dependency requirements of the tasks. [PERSON] et al. [12] further modeled the resource scheduling as a deep reinforcement learning problem that is solved by an enhanced deep Q-network (DQN) [13] algorithm with an independent target Q-network to achieve optimal resource utilization for the whole system. Some scholars have considered the balance of energy consumption and latency in computation offloading. [PERSON] et al. [14] proposed an online learning model for latency and energy consumption to minimize the offloading cost. [PERSON] et al. [15] considered the changes in the communication environment due to vehicle movement and jointly optimized multiple vehicle offloading and bit scheduling to reduce the total vehicle energy overhead. [PERSON] et al. [16] proposed a value-based iterative reinforcement learning (RL) approach to determine a joint strategy for computation offloading and resource allocation, a double deep Q network (DDQN) based approach was developed to minimize the energy consumption of the whole system. [PERSON] et al. [17] summarized the methods related to reducing the energy consumption in wireless sensor networks, and lower energy consumption in sensor networks can save the computational cost in the VEC framework to a great extent, which makes it possible to apply the framework in reality. In order to improve the performance of VEC, some scholars have designed incentive mechanisms to encourage vehicles to participate in edge computing and contribute their idle resources. [PERSON] et al. [18] proposed a distributed vehicle-vehicle offloading architecture that maps task scheduling into a sequential decision problem considering the channel state and the limited resources. In addition, a dynamic pricing scheme was proposed to guide vehicles to contribute the idle computing resources. Considering the importance of service caching in VEC, [PERSON] et al.