Influence Maximization for Emergency Information Diffusion in Social Internet of Vehicles

A variety of emergency events such as traffic accidents often happen in urban areas and the related emergency information needs to be urgently diffused to vehicles that may pass through the accident place to avoid escalation of the event. However, due to the dynamics of vehicles, the information diffusion links between vehicles and roadside units (RSUs) may be unstable, and thus the information diffusion based on fixed RSUs may lead to the low information propagation rate and the limited influence range. To address the above issues, we propose an emergency information diffusion strategy based on the Social Internet of Vehicle (SIoV), where vehicles can build inter-vehicle social relationships without human intervention and exchange emergency information through stable vehicle-to-vehicle (V2V) links. In detail, we design a $b$ -matching based stable link construction algorithm to build stable V2V links by considering the social characteristics and connection probability simultaneously, and then depict the vehicle link graph. Based on this graph, we formulate the information diffusion problem as an influence maximization problem. To solve this problem, we design an SIoV based emergency information influence maximization (SEIM) algorithm to maximize the influence range by selecting some influential seed vehicles and boosting some vehicles’ influence. The theoretical analysis and simulation results show that our algorithms have a lower dissemination delay and larger influence range by sacrificing part of the signaling overheads performance.