# Vehicular Edge-Computing Framework for Making Use of Parking and Charging Electric Vehicles
## 2 Related Work
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Literature** & **Problems and Solutions** & **Results** \\ \hline [PERSON] [6] & A dual-end optimization method is proposed to balance the benefits of the user side and server side. & Reduce costs. \\ \hline [PERSON] [7] & A heuristic algorithm based on dynamic programming is proposed, which can jointly perform task partitioning and offloading in real time, as well as accomplish adaptive decisions at the system and application levels. & Reduce end-to-end latency. \\ \hline [PERSON] [8] & A machine learning-based, region-/context-aware equipped content pre-caching strategy is proposed that addresses the problem of pre-caching of early decisions and predicted results. & Improved pre-caching in VANET to avoid network congestion. \\ \hline [PERSON] [9] & A particle swarm optimization-based approach is proposed to minimize the energy and resource consumption of computational offloading under delay constraints. & Reduce average task completion time and improve resource utilization. \\ \hline [PERSON] [10] & A Deep Reinforcement Learning (DRL) based algorithm is proposed to solve the scheduling policy optimization problem. & Enable tasks with varying degrees of urgency to be completed within time constraints. \\ \hline [PERSON] [11] & A task scheduling algorithm is proposed to rank tasks in order of priority to meet the time constraints and processing dependency requirements of the tasks. & Reduce the average completion time of multiple tasks. \\ \hline [PERSON] [12] & The problem of resource scheduling is addressed by the Deep Q-Network (DQN) algorithm enhanced in deep reinforcement learning. & Achieve optimal utilization of resources. \\ \hline [PERSON] [14] & Limited cellular spectrum and energy supplies restricted. & Minimize the offloading cost. \\ \hline [PERSON] [15] & Address the issue of optimal energy-efficient offloading strategies when the communication environment changes due to vehicles movement. & Reduce total vehicles energy consumption. \\ \hline [PERSON] [16] & A value-based iterative reinforcement learning (RL) approach, named Q-learning, is proposed to determine a joint strategy for computational offloading and resource allocation. & Minimize the energy consumption of the whole system. \\ \hline [PERSON] [17] & Methods related to the reduction of energy consumption in WSNs (a group of sensor nodes capable of sensing various environmental parameters) are summarized. & If applied to the VEC framework, resource consumption can be reduced. \\ \hline [PERSON] [18] & The sequential decision problem of task assignment is solved by reinforcement learning; a dynamic pricing scheme is proposed to guide vehicles to contribute idle computational resources. & Improve task assignment performance. \\ \hline [PERSON] [19] & A software defined network based vehicular edge computing framework is proposed that introduces reputation values to measure the contribution of each vehicle to the VEC. & Reduce delay, increase edge server profits. \\ \hline [PERSON] [21] & A three-stage offloading incentive mechanism based on the Starkelberg model is proposed to obtain the optimal strategy for each participant. & Enhance the utility of tripartite participants. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related works. new energy or electric vehicles, more and more parking and charging sites have been built in cities, especially in the big cities in China. According to the urban development plan of Beijing, there will be 700,000 electric vehicle charging piles in the city by 2025, and the average service radius of electric vehicle public charging facilities in plain areas will be less than 3 km. In the parking and charging lots, the charging piles can collect the information of remaining electric power in each charging vehicle, and control the charging power for the vehicle. Meanwhile, the service providers have developed a management software system to manage and schedule the vehicles for optimal charging.