# Fog Computing for Smart Cities' Big Data Management and Analytics: A Review
## 6 Fog Computing and Data Management Use Cases in Smart Cities  
### Intelligent Transportation Systems and Vehicular Fog Computing  
Transport systems in cities worldwide are adopting digital technologies to deal with the challenges of this vital domain in any city. Intelligent Transportation Systems (ITS) are part of this development and are also part of a broader vehicle automation process. These systems use emerging technologies that allow road vehicles to communicate with other vehicles (V2V) or road users and roadside infrastructure. ITS systems can improve traffic efficiency and road safety in cities and reduce energy consumption and transport emissions. These goals cannot be achieved without improving the quality and reliability of information and ensuring data protection and cybersecurity. Figure 4 illustrates a fog-based intelligent transportation system (ITS). Fog computing provides several benefits to all users of the intelligent transportation system. The vehicles are informed of traffic status and congested roads and are redirected to other routes. They are informed of road accidents and emergency evacuation routes, as well as services such as finding appropriate parking spaces. Traffic lights and traffic signs can be adjusted according to traffic conditions. They can recognize emergency vehicles such as ambulances and police cars and assign them a specific lane. Pedestrians are informed of the best routes to their destinations based on the traffic conditions on the roads and the condition of the sidewalks they have to cross. The Internet of Vehicles (IoV) refers to a network of these various entities, which include vehicles, roadside units, pedestrians, traffic lights, and parking lots. This network provides real-time communication between these entities. To solve the parking problem, [PERSON] et al. [39] proposed a parking service that relies on fog computing to provide drivers looking for parking spaces with real-time information on vacant parking spaces. The service aims to improve the forecast of available parking spaces. Data on the number of vehicles looking for a parking space and the number of currently vacant parking slots are collected by the fog nodes, making predictions and broadcasting the information to the vehicles. Fog computing can also improve transit services by providing up-to-date information on the arrival and departure of public transit services such as buses and trans [40]. In the context of IoV, [PERSON] et al. [41] proposed an architecture that relies on fog computing to share semantic IoV knowledge among fog nodes, using a publish/subscribe model and their proposed ontology, consistently and explicitly. Fog nodes continually collect data from vehicles and smart traffic lights. A publisher fog node is responsible for defining an event (topic) to which other fog nodes can subscribe. The distribution of fog nodes at different localities, such as at a road intersection, helps meet the demand for low-latency service. Urban vehicle networks are an essential element in future intelligent transport systems. They provide support for various mobile services, including information delivery services and content sharing services. However, without effective communication and IT support, these services cannot be put into practice in everyday life and remain in the design phase. [PERSON] et al. [42] proposed a new system, which expands the available resources and increases the achievable capacities using vehicles as a communication and computing platform. They referred to this system as Vehicle Fog Computing (VFC). They described four application scenarios in which moving vehicles and parked vehicles are used as infrastructure. They presented an overview of the VFC paradigm's potential capabilities, which uses vehicles as infrastructure, and the open problems associated with it. Their study demonstrated the benefits of VFC in increasing computational speeds and reducing delays for applications with intensive computing requirements. In another study, [PERSON] et al.