Social Evolving Graph-Based Connectivity Model for Vehicular Social Networks

Abstract

Over the last two decades, social networking over the Internet has attracted the attention of millions of users and becomes a new factor that drives business intelligence and economic growth worldwide. People have strong social instincts and they tend to socialise and communicate with each other in every possible scenario thus, social networks have been extended to different types and forms beyond the Internet. Mobile Social Networks (MSN) represent a new form of social networks that take advantage of the close proximity and the unique features offered by mobile devices to establish social links among mobile users. More recently, Vehicular Social Networks (VSN), a special type of MSNs, have emerged as a new communication paradigm for social networking on the roads. VSNs are decentralised opportunistic communication networks formed among vehicles (Vegni and Loscri 2015), where the communication takes place in three dimensions: human to human, human to machine, and machine to machine communications. The flourish of vehicular networks development over the last decade has made the social interaction on the roads possible promising more enjoyable experience for vehicular travellers.
Prior to discussing VSNs and their connectivity patterns, it is important to develop an understating of vehicular networks on one hand and the human social behaviour in VSNs from a social theory viewpoint on the other hand. Vehicular networks are characterised with high mobility and frequent network topology changes, which make the connectivity between vehicles tends to be vulnerable. On the other side, in addition to being within the communication range of each other, sharing the same social interests and/or characteristics is a prerequisite for any two vehicles to socially interact in VSNs.
This chapter is intended to present a novel social connectivity model for VSNs by utilising the evolving graph theory. First, the properties of VSNs are briefly introduced in the context of vehicular networks. The social metrics of the communicating vehicles are then reviewed using the concepts of social theory along with the conventional connectivity metrics in vehicular networks.
Thereafter, a novel social evolving graph-based connectivity model that considers both social and conventional metrics of the communicating vehicles is developed using an extended version of the evolving graph. Moreover, the proposed connectivity model suggests new social links with vehicles that enter the communication area of other vehicles with similar social interests. Finally, the developed connectivity model is investigated in a highway scenario to demonstrate its abilities
in capturing the evolving characteristics of social interactions among vehicles and selecting the best paths to forward data. Data forwarding decisions are made based on a combination of social and communication metrics of the communicating vehicles. Simulation results showed that the proposed connectivity model facilitates the social interactions among vehicles and is able to establish reliable social paths among the communicating vehicles.