A Simulation Study of Social-Networking-Driven Smart Recommendations for Internet of Vehicles. (arXiv:1907.01101v1 [cs.SI])

Social aspects of connectivity and information dispersion are often ignored
while weighing the potential of Internet of Things (IoT). In the specialized
domain of Internet of Vehicles (IoV), Social IoV (SIoV) is introduced
realization its importance. Assuming a more commonly acceptable standardization
of Big Data generated by IoV, the social dimensions enabling its fruitful usage
remains a challenge. In this paper, an agent-based model of information sharing
between vehicles for context-aware recommendations is presented. The model
adheres to social dimensions as that of human society. 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 was 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.

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