Abstract
Mobile edge computing (MEC) has been recently proposed to bring computing capabilities closer to mobile endpoints, with the aim of providing low latency and real-time access to network information via applications and services. Several attempts have been made to integrate MEC in intelligent transportation systems (ITS), including new architectures, communication frameworks, deployment strategies and applications. In this paper, we explore existing architecture proposals for integrating MEC in vehicular environments, which would allow the evolution of the next generation ITS in smart cities. Moreover, we classify the desired applications into four major categories. We rely on a MEC architecture with three layers to propose a data dissemination protocol, which can be utilized by traffic safety and travel convenience applications in vehicular networks. Furthermore, we provide a simulation-based prototype to evaluate the performance of our protocol. Simulation results show that our proposed protocol can significantly improve the performance of data dissemination in terms of data delivery, communication overhead and delay. In addition, we highlight challenges and open issues to integrate MEC in vehicular networking environments for further research.
Highlights
The major objective of intelligent transportation systems (ITS) is to improve traffic safety, transportation efficiency and driving experiences
This can be explained by the utilization of traffic condition information offered by the network edge in controlling the broadcasting suppression
A noticeable improvement by around 40% in the dissemination overhead is shown in TURBO-mobile edge computing (MEC) compared to the original version of TURBO
Summary
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, 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)
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