# Vehicular Edge-Computing Framework for Making Use of Parking and Charging Electric Vehicles
## 1 Introduction  
With the rapid development of information technology, advanced vehicular applications such as autonomous driving, intelligent cockpit, virtual reality, and so on, are emerging. These computation-intensive vehicular applications require a large number of computing resources, and low-response latency should be ensured. Due to the limited computing resources in vehicles, the vehicle side may be impossible to provide high-quality computing services for these applications. Moreover, although the cloud center has a large number of computing resources, it is far away from the vehicles and the network transmission of huge data would bring high response latency. Deploying the servers at the edge of the vehicles can shorten the communication distance and also provide powerful computing capability for these vehicular applications. Consequently, vehicular edge computing (VEC) [1] has emerged, with communication base stations and servers deployed on the sides of the road, which form a vehicular edge network to provide low latency and highly reliable computing services for vehicles. With the support of VEC, the vehicles can offload computation tasks to the edge servers and then obtain efficient computing services. Generally speaking, the quality of service is affected by the number of deployed edge servers. With the vehicular intelligence applications widely used, the limited resources of edge servers will not be able to meet the increasing demand for computation services [2]. Therefore, it becomes a challenge to expand the VEC resources and optimize the allocation of computing resources. To address the problem of insufficient edge resources in VEC, existing research works dealt with this problem from the following two aspects. Firstly, more lightweight edge servers are deployed. For example, [PERSON] et al. [3] proposed a method to deploy standby servers inroadside units (RSUs) to alleviate the shortage of VEC resources. However, this approach requires much cost for deploying the servers, and with the continuous increasing of service demand, the shortage of standby resources will occur once again. Meanwhile, it also results in a surplus of resources during off-peak periods. Secondly, it taps into the underutilized resources of nearby vehicles. This solution does not require the deployment of additional edge servers and uses the large number of idle computing resources provided by nearby vehicles to assist the edge servers in computation offloading. In recent years, we have found that electric vehicles are not only the traditional vehicles, but are also mobile supercomputers loaded with powerful CPUs, large data-storage units, and advanced communication technology. However, these in-vehicle resources will often be idle and not fully utilized. Therefore, aggregating the idle resources in vehicles for VEC would be one of the ways to effectively extend VEC computing resources. In the literature, [PERSON] et al. [4] made use of parking vehicles to participate in task processing. It is impractical for traditional fuel vehicles to provide computation resources in the parking state, which is costly and unsustainable. However, nowadays, the commercial operation of electric vehicles has built a large number of special parking lots for electric vehicles in various areas of the cities, and many charging piles are deployed in the parking lots for vehicle charging. The charging piles can obtain the remaining power and related information of the vehicles, and the infrastructure deployed in these parking lots provides great convenience for parking vehicles to participate in VEC services. Compared with VEC servers, these parking vehicles have cheaper computing resources. Therefore, with the parking vehicles' computing resources used in VEC, vehicle users can not only have more adequate edge computing resources, but also obtain relatively cheaper computing services. However, how to effectively integrate the idle resources of parking vehicles for edge computing is a problem to be solved and which will be studied in this paper.