# Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
## 6 Open Research IIoT Application Domains, Fog Computing as an Enabler
### Micro-Grids (MGs)
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref. No** & **IiOT Application Domain** & **Case Study: Key Focus** \\ \hline
[122] & smart city & Smart city solutions have been deployed in cities, such as Barcelona and Venice, to make further advancements in e-governance \\ \hline
[123] & smart traffic control and health monitoring & To increase flexibility in a fog computing in the context of Complex event processing (CEP), a case study is presented. The methodology, called “mechanism transitions”, is used to study how and where a query should be processed and how this decision affects the performance. \\ \hline
[124] & smart city & Fog Computing Architecture Network (FOCAN) is presented to give low-latency and energy-efficient solution for smart city applications. It manages different application’s requirements by categorizing the traffic type and its flow. \\ \hline
[125] & city, factory, building, home & Using an open-source platform Distributed Node-RED (DNR), authors have presented how applications can be decomposed and deployed. They build prototype for scalability and dynamic nature solutions using the network simulator Omnet++. \\ \hline
[126] & smart pipeline monitoring & A sequential machine learning algorithm on every layer of fog-cloud architecture, sensors and Markov model are used to monitor, control and detection of hazardous events of a pipeline system. A working prototype was constructed to observe 12 distinct events. This prototype could be used for future city-wide pipeline safety measurements \\ \hline
[127] & smart transportation & The extended policy management to support secure travel to user’s is presented by the authors. Four different route guiding scenarios are explained; namely depending on traffic condition, emergency connected vehicles (ECV), connected vehicle (CV) and probable collision detection. \\ \hline
[128] & smart transportation & Smart transportation framework is proposed for Vehicle to Vehicle (V2V) communication by the authors, on basis of the current traffic situation (road and vehicle’s condition, capacity). \\ \hline
[129] & big data & Case study named as “Streamcloud” is presented to provide real-time energy-efficient solution. \\ \hline
[130] & healthcare & Personalized missing data resilient decision-making approach is validated on a real human subject trial on maternity health. Data missing in critical applications is a very crucial challenge, that needs to be solved. \\ \hline
[131] & healthcare & Table 2 in the mentioned paper gives some projects for healthcare monitoring supported by fog computing, cloud computing, and IoT. \\ \hline
[132] & healthcare & A demo test-bed is developed on edge-IoT architecture for e-healthcare applications. Proposed EH-IoT gives better results towards bandwidth and latency requirements. The article also presents the benefits leveraging from IoT and edge computing from an industrial perspective. \\ \hline
[133] & cardiac diseases & A case study using Electrocardiogram (ECG) feature is discussed in the article to monitor health in real time. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Case studies, Fog Computing as an enabler. number of smart devices results computation delays at cloud also it involves more power consumption. This power consumption can be optimized after load balancing among fog nodes and cloud server. [PERSON] et al. [134] have discussed various ways for the deployment of IoT nodes in an energy-efficient way. The authors have used MGs and fog computing as an enabler with an aim to reduce the energy consumption by the IoT specific applications. Energy consumption of various types has been discussed i.e., energy consumed during computation and balancing traffic on fog as well as the cloud. Renewable energy is stored in MGs, which can be used for further processing. Dynamic decisions such as weather forecasting or renewable energy availability causes dependent energy-saving processes.