# Process Automation in an IoT-Fog-Cloud Ecosystem: A Survey and Taxonomy
## 5 Fog Intelligence and Automation
### Automatic Data Transfer
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Paper** & **Application** & **Sampling Rate Concer or Limitation on Data Filtering** \\ \hline [PERSON] and [PERSON] [42] & health care monitoring & lack of sampling rate tuning in application \\ \hline [PERSON] et al. [37] & gaming & Cloud module dynamicity defined based on number of players but not data sampling \\ \hline [PERSON] et al. [40] & health care monitoring & a sampling rate based on user requests is analysed but not automated \\ \hline [PERSON] et al. [41] & healthcare monitoring & a sampling rate based on user requests is analysed but not automated \\ \hline [PERSON] et al. [43] & waste management application & lack of discussion on sampling rate \\ \hline [PERSON] et al. [44] & smart city: transportation & lack of discussion on sampling rate \\ \hline [PERSON] and [PERSON] [38] & smart city & big data discussion such as data integration,shuffing,cleaning but not data filtering and sampling rate \\ \hline [PERSON] et al. [10] & smart city: shopping application & lack of discussion on sampling rate \\ \hline [PERSON] et al. [45] & smart home & lack of discussion on sampling rate \\ \hline [PERSON] and [PERSON] [46] & smart home & lack of discussion on sampling rate \\ \hline [PERSON] et al. [36] & travel guide & two defined sampling rate on day time and night time \\ \hline [PERSON] et al. [47] & smart city: shopping application & lack of discussion on sampling rate \\ \hline [PERSON] [39] & smart city & an analysis on types of data such as batch,real-time but not sampling rate and its automation \\ \hline [PERSON] et al. [13] & smart city & lack of discussion on sampling rate \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The review and analysis of papers of their concern on data filtering. [PERSON] et al. [52] explain that IoT tasks can be classified into two categories: time-sensitive tasks and batch tasks. The time-sensitive tasks should be hosted in the Fog layer to satisfy QoS timing criteria, and batch tasks are better to transfer to the Cloud data centers. [PERSON] and [PERSON] [46] show which functions need to be transferred to the Fog, and which should be hosted on the Cloud layer. The classifier algorithm divides tasks into those that require fast decision-making and those that need latent big data analysis. They use an IoT game application as a use case to show how data transfer between hosts impacts network usage. [PERSON] et al. [53] define a smart Fog-engine component, which is responsible for data analytics to support decision-making on an IoT application. The decision can automate the data transfer to proper hosts. The Fog layer is considered as the first hosting environment and the Cloud resource as the alternate one. The Fog engine can be a Fog node, smart device, or a network gateway in the Fog layer, which can analyze, process, and manage the hosting environment of a task. Table 4 shows the papers with their techniques and methods to implement automatic data transfer. Decision making on data transfer are based on different classification algorithms. The latency and cost of running each algorithm on the ecosystem should be studied as well.