# Social Internet of Vehicles for Smart Cities
## 3 Vehicle Context-Awareness  
A key aspect in IoV is to enable vehicles to be context-aware, _i.e.,_ to be aware of the circumstances that exist around the vehicle, especially those that are particularly relevant to it [52]. Context-aware systems are those that have the capability to adapt their behaviour to their current contextual environment. Context-awareness in vehicles can be provided by three main subsystems: sensing, reasoning and acting [53, 54].  
The sensing subsystem gathers contextual information from different sensors integrated with the vehicle's OBU. The type of these sensors differs according to the vehicle's requirements, for example location, infrared or ultrasound. In other terms, this phase represents the way context data are collected.  
The reasoning subsystem processes raw data to extract high-level contextual information, such as the driver's situation. Contextual information can be either extracted from a single sensor, defining certain contextual information, or extracted from multiple sensors, defining uncertain contextual information. Detecting driver's fatigue levels is considered uncertain (high-level) contextual information.  
The acting subsystem represents the application enforcer, which provides services to users or other drivers. Disseminating warning messages, in-vehicle alerts and smart assisted parking are examples of high-level applications deployed to prevent accidents and reduce road congestion.  
Several context-aware systems and frameworks for the SIoV have been proposed in the literature. [PERSON] _et al._[55] presented s-frame, a framework for social vehicular networks formed by in-vehicle or mobile equipment used by passengers, vulnerable road users and drivers that supports high-level context-aware applications.  
[PERSON] _et al._[54] designed a VANET on-street context-aware smart parking assisting system, deploying the concept of the centralized InfoStation to locate and reserve a parking slot. This reservation process is dictated by the driver's preferences. All parking zones have a dedicated InfoStation that acts as a terminal, providing wireless coverage over DSRC, as shown in Figure 1.  
Figure 1: On-street parking system scenario.  
[PERSON] _et al._[29] presented a cloud-based context-aware dynamic parking service that provides planning services for traffic authorities, a parking reservation service and context-aware optimization. Their framework allows drivers to park their vehicle alongside the road for short periods, provided this does not impede the traffic flow. To provide this service, the framework considers contextual information, such as time (e.g., rush hours) and road conditions (e.g., the width of a road). Traffic authorities can manage this service dynamically and effectively reduce parking problems in a smart city.  
[PERSON] _et al._[56] designed SocialDrive to allow drivers to disseminate real-time travel information on social networks and to help them understand their own driving behaviour. SocialDrive provides contextual data to drivers to reflect the ultimate driving style, which can reduce fuel consumption by eliminating unwanted habits.