# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 2 Literature Review  
VANETs were firstly introduced in 2001 for providing vehicular communication systems and networking applications [24; 25]. VANETs are then seen as a key component of ITS. In the last two decades, VANETs have been heavily researched as one of the most enabling technologies for connected vehicles [26; 27]. However, the current VANET architecture suffers from miscellaneous issues correlated with connectivity, computation, scalability, flexibility and intelligence [1].  
Vehicular cloud computing (VCC) was introduced [2] to take the advantages of cloud computing in serving VANETs with computational services. The main objective of integrating cloud computing in vehicular environments is to provide dynamic applications that can predict traffic events and adapt to environmental changes. Despite that VCC can greatly improve resource utilization and computation performance in vehicular environments, capacity limitation and transmission delays are considered as serious issues when the cloud servers are far away from the traveling vehicles. More specifically, big traffic data poses a major challenge for the cloud computing paradigm due to low transportation speed. For example, when autonomous vehicles are on the road, each vehicle is expected to generate 1 GB data every second [28]. Instead of directly connecting to the cloud for sending data, vehicles can utilize real-time processing in an edge computing environment for an improved performance in terms of response time, computation efficiency and resources utilization.  
The characteristics of edge computing include low latency and location awareness, wide-spread geographical distribution, mobility, heterogeneity, scalability, the predominant role of wireless access and the strong presence of real-time applications [29]. These characteristics make the edge the appropriate platform for a number of critical Internet of Things (IoT) services and applications, namely, connected vehicle, smart grid, smart cities, and in wireless sensors and actuators networks. In addition, the edge will give rise to new forms of competition and cooperation between providers angling to offer global services [29]. A general survey on edge computing can be found in [3].  
Edge computing refers to \"the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services, and on upstream data on behalf of IoT services\" [28]. The term \"edge\" may refer to any computing resource between data sources and the cloud. In a vehicular environment, a smart traffic light can represent an edge between the vehicles in its coverage and the cloud data center. To clearly understand the difference between conventional cloud computing and the edge computing paradigm, we encourage the reader to refer to [28].  
In the context of vehicular environments, vehicles can directly connect with edges for data collection and service provision, instead of directly connecting to the cloud. As a significant part of IoT, vehicular communication systems would produce and consume data at the edge for an improved performance. In our literature review, we found it promising to integrate edge computing into vehicular networks for a vehicular edge computing (VEC) environment. An example framework for VEC can be found in [30], which aims to increase the computational capabilities of vehicles on the road. It introduces a workflow to support the autonomous organization of edges in vehicular environments. In the remainder of this section, we describe VEC enabling technology, proposed architectures, deployment strategies and potential applications.