# Process Automation in an IoT-Fog-Cloud Ecosystem: A Survey and Taxonomy
## 5 Fog Intelligence and Automation
### Automatic Data Filtering  
[PERSON] et al. [35] discuss how the data from the IoT layer could be filtered by autonomous sensor selection for a specific user request. A technique called Skyline selects the most suitable sensors and filters the most important information and trims the less important information.  
[PERSON] et al. [13] note that for minimizing the energy of different components in the ecosystem, various factors should be considered, such as data sampling from IoT sensors, data conditional gathering, and communication frequency. Data science and feature engineering can support analyzing the data features and qualify any feature data that may increase communication costs and energy. After feature engineering and feature recognition, the framework software will be able to control and manage the energy based on data sampling.  
[PERSON] et al. [36] studies a traveling IoT application. This application suggests and recommends accommodation, transport, and food for the travelers based on their location data in their 5G cellphones. The normal sampling rate of data can be defined as one data point every minute. The automatic data filtering can reduce the sampling rate to one in every 30 min when the tourists are slept and have no movement activity. According to the three mentioned studies, the challenge of automatic data filtering is to make data sampling flexible. Table 2 shows each paper's method, which is used to implement the automatic data filtering.  
One major issue not addressed among reviewed papers is how to adjust the data sampling in an IoT application accordingly. For any IoT application, there are time intervals when high-frequency data samples are required. There are also time intervals when low-frequency data samples suffice. Finding essential factors that determine the frequency of data sampling and density of data is the responsibility of this automation, which is not thoroughly examined by the reviewed studies. As the data sampling rate of IoT sensors can seriously affect the volume of data and complexity of IoT tasks and other automatic functions such as context management, orchestration, and task workflow, this automation should be reviewed and discussed in detail. Therefore, a study on different data sampling rates and their effects on IoT tasks is needed.  
Table 3 shows the selected papers with their studied data filtering management. Many have no concern about the flexibility of sampling rate in their studies. Therefore, there is a lack of analysis among them. [PERSON] et al. [37], [PERSON] and [PERSON] [38], and [PERSON] [39] analyze the number of users and modules but not the data volume and sampling rate. [PERSON] et al. [40], [PERSON] et al. [36], and [PERSON] et al. [41] discuss the sampling rate but without any concern on automatic function.  
\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. [35] & Skyline & sensor selection \\ [PERSON] et al. [13] & Feature engineering & using data science techniques for highest feature selection \\ [PERSON] et al. [36] & Data sampling technique & data analysis and selection based on day time activity \\ \hline \hline \end{tabular}
\end{table}
Table 2: Automatic data filtering methods and techniques.