# Process Automation in an IoT-Fog-Cloud Ecosystem: A Survey and Taxonomy
## 2 IoT and Fog Applications and Architectures  
In this section, some important survey papers that have studied the IoT applications and Fog computing are reviewed by presenting the studies' achievements and limitations. [PERSON] et al. [12] classifies the IoT applications into industrial, health, and smart city. Farm management applications are an example from the industrial domain where sensors measuring different parameters of soil can increase the real-time farm-monitoring knowledge. This knowledge can then help the farmers to make informed decisions that can lead to an increase in the soil quality. Farmers using actuators will be able to take proper actions to meet soil threshold. Remote patient monitoring is another example from the health domain. The sensors measure the patients' life parameters. If a certain threshold is met, real-time preventive actions will be taken, or real-time reports will be sent to the medical staff. Traffic management is a practical application from the smart city domain. By equipping vehicles with Vehicle Speed Sensors (VSS), the generated data will be sent to the Fog layer. The decision-making actions will then manage the traffic lights to avoid congestion on the roads. [PERSON] et al. [13] studies several cases of smart city applications such as smart transportation, smart agriculture, smart health, smart waste management, and smart water management. Their survey depicts a comprehensive study of types of sensors, actuators, and Fog nodes, along with their responsibilities in providing a service in a smart city application. The authors illustrate the types of communications, protocols, and network existing in the smart city application. However, there is no discussion and insight into the process and task automation in the application. [PERSON] et al. [14] analyzes the definitions of Fog layer with a discussion of the taxonomy of Fog computation and architectures. They depict the types of software, platform, and architecture in the ecosystem. Additionally, they illustrate the attributes, key features, advantages, and weaknesses of each Fog-related architecture that serves IoT applications. [PERSON] et al. [15] presents a case study of air pollution in the IoT layer. It focuses on IoT sensor layer functionality for an air-pollution IoT application, showing how to deal with errors in the sensor layer in order to make the IoT application more responsive and accurate. Different attributes of sensors, such as errors, calibrations, and strengths, are discussed as well. The interconnection among sensors is elaborated but with no discussion on sensor collaboration and communication with other IoT layers such as the Fog. The sensor data and the calibration of the sensor layer to a network are studied. However, the article lacks a study on automating sensor selection and sensor data analysis. [PERSON] et al. [16] studied structural differences between Fog and Cloud layers. They addressed some well-known IoT applications such as smart city, smart transportation, and healthcare and the role of Fog computing in managing the data transfer for these applications. [PERSON] et al. [17] discusses the operating systems(OS) of IoT services. The authors review communication techniques and protocols in the IoT layer. The network protocols, along with their packet/frame structure are explained and characterized in the ecosystem. They mainly focus on protocols, packets, and frequencies suitable for IoT applications. [PERSON] et al. [18] reviewed the Message Queuing Telemetry Transport (MQTT) protocol in detail as it is the most important lightweight protocol for IoT. They addressed the non-reliable network, low-bandwidth management, and low-memory as factors, which can lead to high latency in real-time applications. The Machine-2-Machine (M2M) interaction and interoperability are carried out by the MQTT protocol. As the MQTT protocol is a well-known protocol in dealing with heterogeneity in devices and hardware running platforms, dealing with big data needs to be studied as well. [PERSON] et al.