# Social Internet of Vehicles for Smart Cities
## 7 Conclusions  
In this article, we surveyed the key concepts of the Social Internet of Vehicles (SIoV), a new type of network that enables social interactions between vehicles, drivers and passengers in the Internet of Vehicles. More specifically, we reviewed enabling technologies and key components of the SloV and presented context-aware SIoV applications that can be deployed in a smart city. Three main components were introduced that include next-generation vehicles, vehicle context-awareness and SIoV context-awareness applications. Context-aware systems allow vehicles to adapt their behaviour as per the contextual information gathered from different subsystems, such as sensing, reasoning and acting. Several context-aware frameworks are available that support high-level context-aware applications design. Future work should enhance the context-aware systems by adapting existing frameworks and incorporating context-aware applications from different vendors.  
We also identified the types of interactions that can happen between vehicles, drivers and passengers and discussed how social network analysis methods can be used to improve the operation of an SIoV. Using historical data of drivers, vehicles that are moving in a road network can be ranked in terms of their social behaviour. Ranking of vehicles can be used in order to perform efficient message dissemination by selecting the nodes that follow populated roads, so as to alleviate the broadcast storm problem. In order to cope with the interference caused by flooding of messages, clustering of vehicles can also be used, where the social characteristics of the drivers can help in the creation of more stable and robust clustering formations. Combined communication capabilities along with social behaviour of the vehicles can facilitate eco-routing and dynamic charging of electric vehicles. Established social metrics, like degree or betweenness centrality, finally, can be modified in order to cope with the dynamic environment of a vehicular network and provide efficient tools for facilitating the communication among the nodes. Future work should focus on the development of new social metrics that can be dynamic and also able to rank in an efficient manner different entities that co-exist in an SIoV; smart vehicles, passengers, road users and drivers.  
Finally, we discussed the issues of security, trust and privacy that SloVs faces and highlighted how existing security solutions can be applied to these highly dynamic networks. Current security solutions place an imbalanced focus on technical aspects over social aspects. However, with the integration of smart devices and vehicles, the human aspect has become vital. Future research should address the social perspective of security and the interaction between human agents and technical solutions. Trust has been well studied in social networks, but less work can be found in the SIoV. Trust in the SIoV has demonstrated five different characteristics, which are uncertainty, subjectivity, intransitivity, context dependency and non-cooperativeness. Future work should focus on the adaption of the trust decision frameworks, models and systems in social networks, addressing the trust characteristics of the SIoV. Future privacy-enhancing technology in SIoV should be based on existing work for both social networks and vehicular networks. Privacy metrics from both domains need to be considered in order to thoroughly evaluate the upcoming SIoV privacy technologies.