A Simulation Study of Social-Networking-Driven Smart Recommendations for Internet of Vehicles
This work addresses the problem of improving recommender systems in IoV by integrating social aspects, but it is incremental as it builds on existing SIoV concepts with simulation-based testing.
The paper tackled the challenge of incorporating social dimensions into Internet of Vehicles (IoV) for context-aware recommendations, using an agent-based model to simulate information sharing, and found that social tie closure and timing significantly impact information dispersion while fair distribution across competitors is not achievable as the network evolves.
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.