# Process Automation in an IoT-Fog-Cloud Ecosystem: A Survey and Taxonomy
## 5 Fog Intelligence and Automation
### Automatic Task Scheduling  
This component deals with assigning timing resources to IoT tasks for data processing and meeting QoS parameters such as response time. [PERSON] et al. [50] suggest a monitoring system with two checkpoints data: the state of the task's execution and the task's location. The data is recorded and used as metrics for scheduling, replication, and resource assignment.  
[PERSON] et al. [64] study the challenges of task scheduling and the latencies dealing with Electrocardiogram (ECG) data in the healthcare domain. Fog nodes pre-process the tasks by applying networking functions for schedule.  
[PERSON] and [PERSON] [65] introduce the FogR small computation units as other types of Fog nodes for intelligent decision-making. The FogRs are designed to engage the heterogeneous devices to computation in a smart traffic system. The devices such as traffic light, cameras, sensors, and pedestrian cellphones will use their small and light-weight  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Paper** & **Method/Technique** & **Description** \\ \hline [PERSON] et al. [54] & Message minimization & minimizing messages among components for better communication \\ \hline [PERSON] et al. [55] & Machine social ring & society of machines for better communication and automation \\ \hline [PERSON] et al. [56] & Kubernetes & as a technology for automating load balancing \\ \hline [PERSON] et al. [57] & Network ensembling & integrating network protocols for automating messaging \\ \hline [PERSON] et al. [18] & MQTT & a light-weight messaging protocol for flexibility management and automation \\ \hline [PERSON] et al. [10] & Fog node & as a technology for resolving latency in communications \\ \hline [PERSON] et al. [10] & Fog node & as a technology in low connectivity and localization \\ \hline [PERSON] et al. [10] & CDN & a technology in automating context delivery \\ \hline [PERSON] et al. [10] & ICN & a technology to deliver context to mobile users \\ \hline [PERSON] et al. [17] & AMQP & a technology for IoT–Cloud communication \\ \hline [PERSON] et al. [17] & XMPP & a technology for IoT communication with computational overhead \\ \hline [PERSON] et al. [59] & Microservice & a software architecture for communication and automation \\ \hline [PERSON] et al. [60] & Microservice & a software architecture for interconnection, communication, and flexibility \\ \hline [PERSON] et al. [61] & Fuzzy sets and rules & a technology to manage ecosystem communication with humanistic variables \\ \hline \hline \end{tabular}
\end{table}
Table 5: Automatic orchestration and collaboration methods and techniques.  
processing resources in computation. Therefore, the primary responsibility of FogR is to engage more IoT and Fog resources if Cloud computing is not accessible.  
[PERSON] et al. [37] demonstrate the Cloudlets as another solution for increasing the efficiency of an IoT gaming application. As accessing Cloud resources is competitive with high latency, the Cloudlets are introduced to the ecosystem as other types of brokers. Increasing the number of Cloudlets can boost the efficiency of this solution.  
The resource assignment is a vital task for any computational process in the ecosystem. The technologies such as Cloudlets and FogR are implemented for decreasing the task execution latency. In the ecosystem, the resources exist in different types and capabilities, and therefore an accurate resource monitoring algorithm is required for controlling any resource assignment. The cooperation of resource monitoring algorithms and resource assignment algorithms will complement the automatic task scheduling function. Table 6 shows the methods and techniques used for automating the task scheduling and management in the ecosystem.