# Process Automation in an IoT-Fog-Cloud Ecosystem: A Survey and Taxonomy
## 5 Fog Intelligence and Automation
### Automatic Task Workflow  
It is evident that for any action, request, or change in the ecosystem, a series of tasks need to be undertaken. Computer network programmers, IT experts, and IoT skilled people should decide which tasks in the ecosystem should be automated. The correlated tasks and predictable tasks can be designed and executed by the task workflow automation. The task workflow function can be updated by new actions and tasks coming to the ecosystem [66].  
[PERSON] et al. [67] demonstrate a workflow as a way of doing repetitive tasks autonomously. The morning process and healthcare emergencies are two IoT applications shown by proper workflow model design [PERSON] et al. [67]. The morning process is divided into sub-tasks such as initiating robot \(\Rightarrow\) delivering the task of paperboy \(\Rightarrow\) turning the light on \(\Rightarrow\) preparing the coffee. Some of these tasks are divided and converted to an object module with their execution time. For the emergency application, the tasks are considered as health status \(\Rightarrow\) call emergency service \(\Rightarrow\) medical personal arrives \(\Rightarrow\) unlocking door.  
[PERSON] et al. [68] describes the task scheduler as another type of automation in the ecosystem. Here, the Fog node is denoted as a device dedicated for scheduling workloads and applying policies. The workflow task automation can be tied to the context management automation as a note by [PERSON] et al. [67]. Furthermore, [PERSON] et al. [16] remarks the adding scalability attribute to workflow automation when new users are coming to the system or new IoT sensors are plugging in. The interconnection between workflow automation and context management need to be studied. The scalability of tasks and components is a significant challenge when an IoT application is growing in terms of its devices, software, data, and users. In such situations, the tasks and services need to be extended as well to satisfy SLAs.  
Figure 7 shows a diagram of a primary task called workflow 1 divided into 6 sub-tasks. The left-hand side of the figure shows the IoT layer where data streams are coming to the workflow boxes for processing. Tasks 1, 2, and 3 can be executed in the same resource as they have a serial structure. Tasks 3, 5 and tasks 4, 6 are parallel tasks that require parallel resources to be performed as well. This example shows that it is essential to detect the resources needed for tasks before any action. The Fog can then prepare the resources and runs all the tasks at the right time. The yellow circles show the required manual point in  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Method/Technique** & **Description** \\ \hline [PERSON] et al. [50] & Monitoring technique & a technique for task scheduling and resource management \\ \hline [PERSON] et al. [64] & Fog node & a technology for task scheduling \\ \hline [PERSON] and [PERSON] [65] & FogR & a technology for intelligent decision making \\ \hline [PERSON] et al. [37] & Cloudlet & a technology for IoT application efficiency \\ \hline \hline \end{tabular}
\end{table}
Table 6: Automatic task scheduling methods and techniques.  
workflow 1 that should be applied after the execution of task 2, or 3. The grey circle is the endpoint of the main tasks. By increasing the users, requests, or resources, the tasks need to be extended as well, and this is the responsibility of the orchestration and collaboration component. Therefore, a collaboration between workflow automation and orchestration and collaboration automation is necessary. Table 7 shows the methods and technologies of two reviewed papers for conducting the task workflow in the ecosystem.