**3. Fog-cloud architecture to monitor vital signs in smart cities**

Employing several sensor devices to monitor the health parameter of people daily is crucial to track and monitor the spread of new diseases. We developed a monitoring infrastructure for intelligent cities to enhance public health topics. **Figure 2** depicts our vision of an innovative city architecture, where we focus on mobile health, vital signs collection, and artificial intelligence services. In the considered architecture, citizens use wearable devices (such as smart bands or standalone sensors) to send their vital sign parameters into the public health data center in real time. By combining fog and cloud computing, we offer a collection of health services to patients/users in a

**Figure 2.**

*Smart city architecture focuses on monitoring patients' health parameters. People wear sensors that transmit health parameters to a fog-cloud infrastructure that provides health services.*

new paradigm. Here, the public health system provides intelligent services in proactive and on-demand services.

We envisage the functioning of the services by using both artificial intelligence and inferential statistics. The main idea is to enable a set of capabilities for end-users. The most used functionality is event prediction, which analyzes a collection of data in the past (where each element represents a vital sign data and a timestamp), employs a prediction engine, and presents as output a forecast of an event for the future. We can implement event prediction using logistic and linear regression, ARMA, ARIMA, random forest, or neural networks. The second type of event refers to correlations. For example, they can be implemented using confusion matrices, cosine's rule, and Pearson's coefficient. The third type of service uses data classification. Here, we have a learning process that helps build a learning model, enabling us to classify health situations. Classification is commonly deployed with Support Vector Machine and k-nearest neighbors.

Yet, pattern recognition is another type offered in the proposed architecture of health services. The main idea is to analyze raw data to perceive clusters with standard features. To implement pattern recognition, at this moment, we plan to use Neural Networks and K-means clustering. For example, a health surveillance system can forecast the health disorders of a person wearing smart bands. Thus, the system can proactively call an ambulance and schedule appropriate human resources in hospitals to support a patient. Using pattern recognition, we can identify sections of a city with a more considerable risk of a particular disease. Moreover, by blending vital signs and a geolocation system, we architecture can analyze the efficiency of lockdown policies. *On Defining and Deploying Health Services in Fog-Cloud Architectures DOI: http://dx.doi.org/10.5772/intechopen.109570*

#### **Figure 3.**

#### *Edge architecture and deployment proposal.*

In addition, in the case of a pandemic scenario, we can generate cost-efficient procedures to reopen cities in a secure and timely way.

Our architecture can support different wearable devices, each functioning with a particular IoT protocol. In this way, we present in the Edge Controller a middleware to support device heterogeneity. It acts as a gateway, receiving different types of inputs and outputting a uniform protocol for the upper layers. We can consider a collection of factors that need to be considered when deploying health monitoring devices for remote patients. For example, some topics that should be considered are user authentication, data regulations, data privacy, API availability, data extraction mechanism, data processing system, and information transmission (including direct and indirect communication directives and intermediary brokers).

In our architecture, an Edge Controller is placed near the patient to collect, process, and transmit data to the Fog infrastructure. Also, the architecture envisages a mobile Edge Controller, enabling a user to change effortlessly from one Fog Node to another. **Figure 3a** illustrates the Edge Controller architecture and possible deployments at the patient's site. **Figure 3a** presents a collection of components and their communication to process sensor data. In addition, a viable deployment is depicted in **Figure 3b**.
