# Efficient Collaborative Edge Computing for Vehicular Network Using Clustering Service
###### Abstract  
Internet of Vehicles applications are known to be critical and time-sensitive. The value proposition of edge computing comprises its lower latency, advantageous bandwidth consumption, privacy, management, efficiency of treatments, and mobility, which aim to improve vehicular and traffic services. Successful stories have been observed between IoV and edge computing to support smooth mobility and the use of local resources. However, vehicle travel, especially due to high-speed movement and intersections, can result in IoV devices losing connection and/or processing with high latency. This paper proposes a Cluster Collaboration Vehicular Edge Computing (CCVEC) framework that aims to guarantee and enhance the connectivity between vehicle sensors and the cloud by utilizing the edge computing paradigm in the middle. The objectives are achieved by utilizing the cluster management strategies deployed between cloud and edge computing servers. The framework is implemented in OpenStack cloud servers and evaluated by measuring the throughput, latency, and memory parameters in two different scenarios. The results obtained show promising indications in terms of latency (approximately 390 ms of the ideal status) and throughput (30 kB/s) values, and thus appears acceptable in terms of performance as well as memory. cloud; edge computing; vehicle; clustering; VEC +
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data uploading to the cloud. The general benefit is gaining high network reduction with bandwidth efficiency, response time, and low delay [10]. Edge computing is designed to be located as near to the data source of end-users as possible. This frequently results in the tasks of storing used/retrieved data and partial computing tasks being carried out in the edge computing node, which in turn reduces the intermediate data transmission process and communication costs. In the end, the cloud service is pushed to the network edge, and thus the resources required for computation and storage are moved to the proximity of the end device to achieve reduced latency and energy conservation to a large extent. One important sectors that benefits the most from the advantages of all the aforementioned edge computing features is the Internet of Vehicles (IoV). The IoV, by nature, requires high-speed, accurate, and scalable systems while dealing with contemporary traffic challenges. In IoV technology, the vehicle is presented as a smart object supported with sensing equipment, processes, and storage, that is connected to other nodes such as those of the edge, cloud, or other vehicle(s), situations which constitute the phenomenon which is called vehicle-to-everything communication (V2X) [11]. Due to the V2X communication model, the IoV may use various technologies for connection, including wireless LAN (WLAN) networks and cellular networks such as 5G or Bluetooth. Several applications have been implemented in IoV with the support of passengers and traffic management managers, and in the urban traffic environment to provide network access for drivers [12]. Annual reports indicate millions of injuries caused by traffic accidents, and that 90 percent of these accidents are caused by human error. Leveraging the ever-increasing development of IoT and mobile networks, the Internet of Vehicles (IoV) is expected to decrease traffic accident rates, and meet the needs of traffic efficiency and traveling convenience. However, this kind of service requires ultra-low latency (for, e.g., warnings of intersection collision dangers and merging arbitration) for communications, and fast processing, which is considered a big challenge for IoV applications [13]. In addition, issues remain unresolved in the traditional network, including the need to meet various pressing demands in heterogeneous and complex vehicular scenarios. This paper proposes a collaborative mobile edge computing framework that guarantees and enhances the connectivity between vehicles (IoV sensors) and cloud servers by utilizing the mobile edge computing paradigm in the middle.