# 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
The system's evaluation in a particular scenario showed that the overall delay obtained with fog computing was about six times shorter than when the end node conveyed data directly to the cloud. Fog-based solutions have been proposed for the classification of patients with infectious diseases. For example, a fog-based mobile health system was proposed by [PERSON] et al. [56]. This system stores personal Zika virus symptoms and risk zone information on fog servers distributed at different locations in a risk area. Zika is one of the fastest spreading infectious viruses posing new threats to public health around the world. The authors implemented a fuzzy k-nearest neighbor (FKNN)-based classification system to classify users as uninfected or infected based on their Zika virus symptoms. The cloud storage and processing component consists of different modules: data collection, information protection, FKNN-based classification, geographic positioning system (GPS)-based risk assessment, and health communication. The fog server is constantly capturing user data and mosquito sensor data, so that any newly infected user or risk site is automatically identified. [PERSON] et al. [57] proposed similar work to classify dengue patients into three classes (infected, uninfected, and severely infected). Dengue fever is a common viral disease transmitted by mosquitoes. The proposed architecture is a three-layer architecture that relies on fog computing as the intermediate layer between the IoT infrastructure and the cloud. The fog layer allows large amounts of dengue data to be stored after pre-processing. The authors' study found that the use of fog infrastructure resulted in reduced system latency and improved response and execution times for patient classification without affecting accuracy. As we mentioned earlier, fog computing is not a substitute for cloud computing. Several efforts investigated the interoperability between cloud and fog computing in the context of healthcare systems [58; 59; 60]. [PERSON] et al. [60] proposed a healthcare fog-cloud architecture to reduce applications' delays and costs and meet their time constraints. They stated that using their proposed algorithms allows for dynamically distributing health tasks between fog nodes and cloud servers. Fog nodes are in charge of executing computation tasks such as data analysis and context management. The scheduling module formulates a mapping between tasks to be executed on fog nodes, represented as a bipartite graph, and cloud servers. The task dispatcher module assigns scheduled tasks to fog and cloud nodes. [PERSON] et al. [58] presented a three-tier architecture for monitoring patient health using fog computing and cloud computing. The architecture consists of the sensor infrastructure, fog computing resources, and cloud computing servers. The fog tier aggregates data received from edge devices. Then, it assigns processing and data analysis tasks to fog nodes and edge devices using a task-scheduling algorithm. [PERSON] et al. [59] also proposed a fog computing-based three-tier architecture to help caregivers, clinics, and hospitals offer smart health services to their patients. The architecture consists of a medical devices layer, a fog computing layer, and a cloud computing layer. It allows for the implementation of different operations in healthcare providers' data pipeline. These operations include data collection from medical devices, data processing and analysis in fog resources, and big data analytics on cloud servers. Table 2 summarizes the above fog-based applications in healthcare and Figure 5 shows the typical components of our proposed fog-cloud-based architecture for healthcare. Several fog nodes can be deployed at various health facilities in a city. They can even be deployed in ambulances to handle emergency situations.