IoVs have been envisioned to improve road safety and efficiency, and provide Internet access on the move, by providing a myriad of safety and infotainment applications to drivers and passengers. However, with limited spectrum resource, harsh wireless channel, and variable vehicle density, IoV communication faces severe challenges to achieve scalability, efficiency, and reliability. In this article, we propose a context-aware IoV paradigm design to enhance the communication performance, where the high-level contextual information is utilized to bring intelligence in the design. Specifically, through big data analytics on large-scale IoV communication traces collected from an extensive experiment conducted in Shanghai, we investigate the impacts of different contextual information on V2V communication performance. We reveal that among many types of contextual information, the NLoS link condition is a major one that significantly affects V2V link performance. Based on that observation, we discuss three critical but challenging communication paradigm designs with context awareness of V2V link conditions: smart medium resource allocation, efficient routing establishment, and reliable safety message broadcasting. Furthermore, we present a case study of a cooperative beaconing scheme, where machine learning methods are utilized to learn the real-time link contextual information, and vehicles in deep NLoS condition choose helpers to enhance the overall beaconing reliability.

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