Internet of Vehicles: Sensing-Aided Transportation Information Collection and Diffusion

In view of the emergence and rapid development of the Internet of Vehicles (IoV) and cloud computing, intelligent transport systems are beneficial in terms of enhancing the quality and interactivity of urban transportation services, reducing costs and resource wastage, and improving the traffic management capability. Efficient traffic management relies on the accurate and prompt acquisition as well as diffusion of traffic information. To achieve this, research is mostly focused on optimizing the mobility models and communication performance. However, considering the escalating scale of IoV networks, the interconnection of heterogeneous smart vehicles plays a critical role in enhancing the efficiency of traffic information collection and diffusion. In this paper, we commence by establishing a weighted and undirected graph model for IoV sensing networks and verify its time-invariant complex characteristics relying on a real-world taxi GPS dataset. Moreover, we propose an IoV-aided local traffic information collection architecture, a sink node selection scheme for the information influx, and an optimal traffic information transmission model. Our simulation results and theoretical analysis show the efficiency and feasibility of our proposed models.