# 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
's proposed architecture uses vehicular fog computing for distributed traffic management in real time [43]. At the center of each region of the city,  
Figure 4: Fog computing in Intelligent Transportation System. a cloudlet component manages the messages uploaded by vehicles. Data received from the vehicles are processed at the cloudlet layer, then delivered to the cloud layer. Vehicles and roadside unit (RSU) devices in the wireless communication range of vehicles form the fog layer. Moving vehicles and parked vehicles near RSUs form fog nodes. Data sensed by vehicles can be uploaded to RSUs. By balancing the load between the cloudlet and fog nodes, the architecture objective is to minimize the response time of the events collected and reported by vehicles. Moreover, [PERSON] et al. [44] proposed a VFC model, which involves deploying fog nodes on some connected vehicles, such as taxis and self-driving buses. These mobile vehicular fog nodes provide computation and communication capabilities to applications that require these resources on the go. Static fog nodes at the edge of the network and mobile vehicular fog nodes provide on-demand fog computing capabilities. Vehicular fog nodes locally process data (such as real-time video) sensed by vehicles, and typically host applications with stringent latency and privacy requirements. If they are overloaded or vehicles are not within the V2V communication range, their workload may be offloaded to nearby cellular or vehicular fog nodes. [PERSON] et al. [45] highlighted the importance of IoV in an autonomous platoon scenario, where sensing data are highly time sensitive and massively shared. They proposed a new VFC architecture for autonomous driving vehicles that cooperate using their sensing capabilities. The architecture simulates platoon vehicles as a vehicular fog where the lead vehicle, with powerful sensing abilities, is the server, and the following vehicles are the fog nodes. The authors stated that their simulation results showed that their proposed VFC architecture helped achieve a 90% sensing coverage ratio in the platoon scenario. In the context of electric vehicles, [PERSON] et al. [46] addressed the computational and charging problems that hinder the successful implementation of an \"autonomous electric mobility on demand\" service (AEMoD). Their solution used a fog-based architecture to improve local management operations, minimizing the expected response time (elapsed time from customer request to vehicle dispatching) and ensuring an efficient charging strategy (finding a nearby charging station). Moreover, [PERSON] et al. [47] used fog computing to develop an energy trading system for Plug-in Hybrid Electric Vehicles (PHEVs). Their solution promotes the balancing of the energy market based on a local V2V energy trading architecture to reduce time costs and energy losses. [PERSON] et al. [48] described the IoV environment, big data characteristics in ITS, and some real-time big data analytics solutions. Furthermore, they discussed the opportunities and challenges in the IoV environment concerning the implementation of fog-based solutions and real-time big data analysis. In addition, the authors in [49] proposed a fog computing system that combines the computing capacity of congested local vehicles and remote data centers to better meet the demand for vehicles in terms of computational capabilities. Using an immigration-death model, they predicted a vehicular fog's potential computability and observed the relationship between computational capacity and vehicular fog radius. In addition, they studied the statistical characteristics of computing capacity generated by vehicular fog across the city and observed their spatial distribution using visualization. Table 1 summarizes these afortentioned fog-based applications in transportation.