# A Simulation Study of Social-Networking-Driven Smart Recommendations for Internet of Vehicles

## TL;DR

This paper presents an agent-based simulation model for social-networking-driven recommendations in the Internet of Vehicles, highlighting how social tie closure affects information dissemination and recommendation fairness.

## Contribution

It introduces a novel agent-based model that incorporates social dimensions into IoV information sharing and analyzes the impact of social tie dynamics on recommendation effectiveness.

## Key findings

- Closure of social ties significantly influences information spread.
- Network evolution affects the fairness of vehicle recommendations.
- Timing of social tie formation impacts information dispersion.

## Abstract

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.