# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 1 Introduction  
The major objective of intelligent transportation systems (ITS) is to improve traffic safety, transportation efficiency and driving experiences. Despite the fact that vehicular ad hoc networks (VANETs) have been viewed as one of the most enabling technologies for connected vehicles in the context of ITS, huge amounts of traffic data bring serious challenges to VANETs due to issues correlated with connectivity, scalability, flexibility and intelligence [1]. To take the advantages of cloud computing in serving VANETs with computational services, vehicular cloud computing (VCC) was introduced [2]. 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. VCC has greatly improved resource utilization and computation performance in vehicular environments, however, capacity limitation and networking transmission delays are being considered as serious issues, especially when the cloud servers are far away from the vehicles on the road. To bring computing capabilities and IT service environment closer to endpoints, mobile edge computing (MEC) has been recently introduced [3]. MEC provides low latency responses, high bandwidth, and real-time access to network information via applications and services. These characteristics enable MEC to offer an ideal platform for vehicular networks, which are highly dynamic environments that essentially require supporting real-time data. Due to the characteristic of proximity, MEC environment can provide fast responses in the context of connected vehicles, which are currently being anticipated to replace smartphones as the computing devices of the next decade [4]. Several attempts have been made to integrate MEC in the context of ITS, including new architectures [1; 5; 6], communication frameworks [5], deployment strategies [7] and applications [1; 8; 9]. Nevertheless, to the best of our knowledge, none of the existing work has presented a detailed implementation for integrating MEC at the application level and characterizing its performance impact. Taking this gap into account, this paper mainly aims to serve as a bridge for researchers who plan to move their proposed solutions from conventional VANETs in the direction of MEC. To achieve our main objective, we propose an edge-based data dissemination protocol which addresses efficiency and scalability in vehicular networks. Efficiency in this work refers to providing full coverage without extra communication overhead, whereas scalability refers to maintaining low data redundancy and delay under different traffic conditions. Existing data dissemination protocols often aim to reduce data redundancy. For an improved performance, this reduction should consider the number of vehicles on the road (which we refer to as traffic density) to control the selection of forwarding vehicles. This is due to the fact that selecting more forwarders than necessary would dramatically increase data redundancy and waste the limited channel bandwidth. To provide a traffic density estimation, the majority of existing protocols exploit beaconing with a fixed period [10; 11; 12; 13; 14; 15; 16; 17; 18; 19], which has several drawbacks on the networking performance, such as wasted bandwidth and increased network congestion [20]. Other protocols which do not rely on beacons usually ignore the condition of the traffic, and therefore, they do not scale well under high-density scenarios [21; 22; 23]. In contrast, our edge-based protocol exploits an EC architecture for traffic condition estimation. This estimation is utilized in controlling data dissemination for an improved efficiency and scalability. In summary, the contributions of this work are three fold:  
* First, we provide a review of the current efforts to integrate MEC in vehicular environments, with a classification of potential applications and a description of the associated challenges and issues for further research.