# Fog Computing for Smart Cities' Big Data Management and Analytics: A Review
## 6 Fog Computing and Data Management Use Cases in Smart Cities
### Fog Computing in Smart Healthcare
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Application** & **Computing Model (Fog/Cloud)** & **Fog Nodes’ Role** & **Benefits** \\ \hline Parking service [39] & Fog & Collect data on the number of vehicles looking for a parking space & Providing drivers with real-time information \\ \cline{3-5}  & & Collect data on available parking slots & Enhancing the prediction of vacant parking lots \\ \hline Transit services [40] & Fog & Provide up-to-date information on the arrival and departure of public transit services such as buses and trams. & Enhancing the prediction of vacant parking lots \\ \hline Sharing of IoV semantic knowledge [41] & Fog with Publish/subscribe & Collect data from vehicles and smart traffic lights & Providing service & low-latency service \\ \hline Vehicles as infrastructure for computation and communication (VFC) [42; 43; 44; 45] Autonomous platooning vehicles [45] & Fog/cloud 3-layer architecture [43] & Moving and parked vehicles act as fog nodes & Increasing speeds & computing speeds \\ \hline Autonomous vehicles [45] & & Sense road events and upload data to RSUs & Decreasing delays for applications with intensive computations needs & Decreasing delays for applications with intensive computations needs \\ Autonomous vehicles [45] & platooning & Support local processing of sensed data & Minimizing response time \\ \cline{3-5} Autonomous vehicles & driving & Host applications with privacy requirements and strict latency & \\ \cline{3-5} Autonomous vehicles & driving & Real-time processing of traffic videos & \\ \hline Autonomous electric mobility on demand [46] & Fog & Improve local management operations & Minimizing response time \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fog computing based transportation applications. Most surveys suggest that cloud computing may not be suitable for time-sensitive healthcare applications such as remote patient monitoring applications, which require immediate actions from physicians for unexpected situations. Transferring data to cloud servers for analysis and processing and waiting for the results could not be tolerated in such circumstances. With the growing data management capabilities of fog nodes, advanced analytics, including event processing and machine learning, could be performed at the edge, improving network conditions and end-to-end latency. Furthermore, by using fog services, health systems could have a dashboard providing real-time information on medical equipment and patient conditions. Thus, remedial actions could be taken before accidents or adverse conditions occur. The potential benefits of edge and fog computing and the advancement made in artificial intelligence and data analytics spurred many efforts to harness IoT, fog computing, cloud computing, and data analytics in healthcare systems. [PERSON] et al. [54] studied several health applications, which they categorized by use case class and deployment scenario. Based on this categorization, they concluded that several applications could benefit from the deployment of fog computing solutions in health facilities. The majority of ubiquitous healthcare applications usually need to be running somewhere between the physical infrastructure and the cloud. The authors provided an inventory of application tasks with a description of where they could run in a fog/cloud architecture. [PERSON] et al. [55] proposed Emergency Help Alert Mobile Cloud (E-HAMC), an alert and emergency management architecture that uses fog and cloud computing. The proposed infrastructure aims to address different types of emergencies in a very simple and effective way. It is designed to optimize the emergency notification process. Emergency data are communicated to fog nodes, which alert the emergency response services and the victim's family members. Fog computing is integrated into the system to allow for the offloading of resource-intensive tasks and data pre-processing to fog nodes.