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An Agent-Based Simulation of an Adaptive Social Internet of Vehicles Recommendation System

EasyChair Preprint no. 1887

6 pagesDate: November 8, 2019


To manifest numerous heterogeneous electronic devices of the futuristic Internet of Things (IoT) as an ensemble, the factors of connectivity and interaction/ information dispersion are (if not more) important as sensing / actuating, context-awareness and services provisioning etc. Internet of Vehicles (IoV) is turning out to be one of the first notable examples of IoT. Very rapidly, the meanings of these factors are changing due to the evolution in technologies from physical to social domain. For example, Social IoV (SIoV) is a term used to represent, when vehicles build and manage their own social network. Towards these futuristic systems, in addition to physical aspects, the social aspects of connectivity and information dispersion should also be explored. 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 aspects are modeled under reasonable connectivity and data constraints. The simulation results reveal that the closure of social ties and its timing impacts the 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, the recommendations guaranteeing a fair distribution of vehicles across equally good competitors is possible.

Keyphrases: adaptive behavior, agent-based model, Internet of Things, Recommendation System, Social IoV

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Arshad Muhammad and Kashif Zia},
  title = {An Agent-Based Simulation of an Adaptive Social Internet of Vehicles Recommendation System},
  howpublished = {EasyChair Preprint no. 1887},

  year = {EasyChair, 2019}}
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