# Efficient Collaborative Edge Computing for Vehicular Network Using Clustering Service
## 3 Related Work  
Cloud computing technologies have been studied and analyzed in several fields. Vehicular edge computing has been evaluated as a network traffic system which is widely involved in this domain. Meanwhile, several research activities have been conducted with  
Figure 2: Architecture of vehicular edge computing. ideas, designs, and implementation. The authors in [23] divided the area on the road into several \"lane section IDs\" and assigned it to the corresponding edge server. The said strategy was implemented over a dynamic map system. Then, all vehicles are connected with edge servers by linking multiple edge servers. The main achievement in this work is that of the reduction in the unstable radio traffic signals which aims to aggregate vehicle data with no effect. The evaluation has been focused on the effectiveness of the load balancing and the scalability of the dynamic map system in multiple edge servers. The authors in [18] proposed a novel method to achieve high levels of collaboration among different edge computing anchors by introducing a novel collaborative VEC framework, called CVEC. In particular, CVEC aims to enhance the scalability of vehicular services and applications in two directions, with horizontal and vertical collaborations. In addition, the authors discussed the architecture and all its possible cases with all technical enablers to support the CVEC. The noticeable results collected from this work are the low execution delay, acceptable times for delivering and offloading to the cloud, and time computation at the cloud. In [24], the authors proposed a mobile healthcare framework utilizing an edge-fog-cloud collaborative network. Health monitoring was parameterized by using edge, fog, and cloud at the far side for further data analysis such as abnormal status. The difference in the patient position during transportation is critical and fatal in emergency cases. Due to low data delivery in health-related applications and connection interruption, the authors encouraged that the mobility information of the end-user be considered along with a pattern detection scheme inside the cloud. This supports users with nearby health centers. The collected results obtained by theoretical analysis can be observed to demonstrate that the proposed framework minimizes the energy consumption and delay of IoT devices. Other interesting results from the experimental analysis of the proposed module performed with recall value, high precision, and time-efficiency are compared to those of existing models. The work in [25] aimed to propose an efficient IoT architecture, mobility-aware scheduling, and allocation protocols for healthcare. The authors proposed a method to support the smooth mobility of patients through an adaptive received signal strength (RSS) by utilizing the hand-off technique. The proposed architecture dynamically ensures the distribution of healthcare tasks among fog/cloud servers. The authors implemented the idea by using a mobility-aware heuristic-based scheduling and allocation approach (MobMBAR). In particular, the MobMBAR aimed to balance the task execution distribution based on the patients' mobility and the spatial residual of sensed data. The results found from this work claim to reduce the total scheduling time by accounting for most task features including the critical level of individual tasks and the higher response time during the reallocation phase. This work has validated the performance by comparing it with existing solutions using simulations. The authors in [11] reviewed and addressed the gap between IoV and IoFV by presenting an in-depth analysis of the key differences between them. The major focus of this paper is an examination of the technological components, communication protocols, network infrastructure, data management, applications, objectives, challenges, and future open issues and trends related to both IoV and IoFV. The paper engages in a sufficient analysis of machine learning, artificial intelligence, and blockchain.