Distributed Vehicular Computing at the Dawn of 5G: a Survey
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Recent advances in information technology have revolutionized the automotive industry, paving the way for next-generation smart and connected vehicles. Connected vehicles can collaborate to deliver novel services and applications. These services and applications require 1) massive volumes of data that perceive ambient environments, 2) ultra-reliable and low-latency communication networks, 3) real-time data processing which provides decision support under application-specific constraints. Addressing such constraints introduces significant challenges with current communication and computation technologies. Coincidentally, the fifth generation of cellular networks (5G) was developed to respond to communication challenges by providing an infrastructure for low-latency, high-reliability, and high bandwidth communication. At the core of this infrastructure, edge computing allows data offloading and computation at the edge of the network, ensuring low-latency and context-awareness, and pushing the utilization efficiency of 5G to its limit. In this paper, we aim at providing a comprehensive overview of the state of research on vehicular computing in the emerging age of 5G. After reviewing the main vehicular applications requirements and challenges, we follow a bottom-up approach, starting with the promising technologies for vehicular communications, all the way up to Artificial Intelligence (AI) solutions. We explore the various architectures for vehicular computing, including centralized Cloud Computing, Vehicular Cloud Computing, and Vehicular Edge computing, and investigate the potential data analytics technologies and their integration on top of the vehicular computing architectures. We finally discuss several future research directions and applications for vehicular computation systems.
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