# A Comprehensive Study of the Use of LoRa in the Development of Smart Cities
## 4 Results of the Analysis of the Selected Works
### Analysis of the Application Layer
These types of sensor have had great momentum with the development of  
Figure 11: Components of IoT ecosystems in smart city applications. RFID:  
valuable applications such as smart parking (sensors are used for the detection of available parking spaces) and security systems in smart home solutions. The second component, which we have called \"Complementary Technologies\", considers those devices used to obtain additional data for specific applications. In this analysis, we identified three groups of elements. The first one includes local-area coverage devices that allow the determination of patterns of mobility of people through the use of iBeacon. The second group includes those devices of personal-area coverage that allow getting values of physiological parameters of people, such as breath rate and blood pressure. In this group, elements related to wearable devices and biomarkers are included. The third group includes devices of wide area coverage that allow increasing the sources of information to complement the data obtained by IoT devices. For example, the use of drones allows a system to get geospatial images of cultivation areas that, together with the pH and temperature values, would allow determining the cultivation conditions with more accuracy. The third component, called data science, considers the techniques used for analyzing the data obtained from the other components. It includes the machine learning technologies used for determining patterns of behaviors (classified as normal or abnormal). It gives to the citizens or city managers an extra perspective for taking decisions. In the case of agriculture, the detection of an anomalous behavior could be related to a possible plague that affects crops. The data generated by IoT devices, sensors, and IT supplementary devices are large, so it is necessary to use cloud or fog computing platforms to store and process them. Since sending all the data to cloud platforms can be expensive or can generate delays, the use of fog computing platforms has grown, becoming more relevant in recent years. The fourth component is focused on the accessibility of data for citizens and city managers. IoT solutions must generate information for improving the decision-making process. Therefore, it is important to deliver the appropriate information to the concerned people using, e.g., SMSs, mobile applications, and web portals. The way of presenting the information must consider aspects of user experience and accessibility. Figure 12 shows an example of a system. In this example, sensors use a Local ID for identification and the information gathered by sensors are sent to the IoT gateway (GW) in fixed intervals of time. The IoT GW receives the Local ID sensor and sensed values which are redirected to FaaS, Fog, or Cloud computing infrastructures. The values sent by the sensors are stored in local database for data analysis. Then, based on the result of the analysis, control messages are sent to the actuators [18]. Figure 12: Technological components of an IoT ecosystem. For an easy expansion and optimization of communication, a lightweight protocol is used. The most popular are the Message Queuing Telemetry Transport (MQTT) and the constrained application protocol (COAP), which are both designed for resource-constrained devices. MQTT supports multiple nodes communicating with a central broker. Information from the broker can be accessed in real time by the user using PCs or mobile devices [22]. The following subsections describe the operation process between the different components of the IoT ecosystem in relation to smart city subcategories.