Agent-Based Model of Smart Social Networking-Driven Recommendations System for Internet of Vehicles

Social aspects of connectivity and information dispersion are often ignored while weighing the potential of Internet of Things (IoT). Assuming a more commonly acceptable standardization of Big Data generated by Internet of Vehicles (IoV), the social dimensions enabling its fruitful usage; emerging as Social IoV (SIoV); remains a challenge. In this paper, an agent-based model of information sharing (for context-based recommendations) of a hypothetical population of smart vehicles is presented. Some important hypotheses are tested under reasonable connectivity and data constraints. The simulation results reveal that closure of social ties and its timing impacts dispersion of novel information (necessary for a recommender system) substantially. It is also observed that as the network evolves as a result of incremental interactions, recommendations guaranteeing a fair distribution of vehicles across equally good competitors is not possible.