Mobile edge computing for V2X architectures and applications: A survey

In mobile environments, with the help of larger bandwidths and cloud computing solutions, any task can be offloaded from a mobile user equipment to be handled remotely. However, even though this process is accelerated with every cellular generation, with 5G being no exception, offloading to a faraway centralized cloud implies non-negligible delay. To tackle this issue concerning delay-sensitive applications, mobile edge computing, now denominated as multi-access edge computing (MEC), was brought to light. With cloud resources brought closer to the edge of the network, MEC greatly reduces task offloading delay, thereby striving to satisfy the constraints of real-time applications. As highly demanding mobile applications, vehicular networks are a target to be addressed in terms of performance, especially communication and computation delay. In this article, we establish the specificities of MEC when applied to the Internet of Vehicles (IoV), and survey recent papers studying implementations of MEC relevant to real-time vehicular considerations. We categorize these latest V2X architectures so as to unveil the mechanisms behind their improved performance: network availability and coverage, reliability and loss of network connectivity, large data handling and task offloading. This survey not only provides an initial understanding of the state-of-the-art advancements in the field of MEC-enabled vehicular networks, but also raises open issues and challenges that need to be addressed before enjoying the full benefits of this paradigm.

Automated summary

This article discusses the application of mobile edge computing (MEC) in vehicular networks. By bringing cloud resources closer to the edge of the network, MEC reduces the delay in task offloading and satisfies the real-time requirements of applications. The article categorizes the latest V2X architectures and explores the impact of network availability, reliability, large data handling, and task offloading on performance.