## 2 Edge Cloud Deployment Areas and Use Cases  
Resource management in edge cloud is extremely important. Software defined networks (SDNs) rapidly captured attention in data center network, making them a potential technology for edge cloud controllers. The key principle of the SDN is the separation of the programable control plane and forwarding plane. Recently, many researchers have proposed the use of artificial intelligence (AI) and machine learning (ML) in SDN controllers to improve resource allocation, security, traffic management, and other domains [17; 18]. Therefore, a programmable intelligent SDN controller can enhance the use of network resources by learning the global view of the network and enabling data-driven decision making. Table 2 shows the potential use cases of IoT and edge cloud, and some corresponding key challenges. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Industry**} & \multirow{2}{*}{**Use Cases**} & \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Killer Apps**} & \multicolumn{6}{c}{**Challenges**} \\ \cline{6-11}  & & & & **R** & **Bw** & **L** & **St** & **Sa** & **Ra** \\ \hline \multirow{2}{*}{Restaurants} & \multirow{2}{*}{Forecast food preparation} & \multirow{2}{*}{Store} & Video & & & & & & \\  & & & Analytics, & Y & & & & & \\  & & & Data Analysis & & & & & & & \\ \hline \multirow{2}{*}{Retail} & Monitoring, tracking customers, & \multirow{2}{*}{Store} & Video & & & & & & & \\  & and improving sales & & & Analytics, & Y & Y & & & & \\  & & & Data Analysis & & & & & & & \\ \hline \multirow{2}{*}{Gas Station} & \multirow{2}{*}{Detect safety hazards} & \multirow{2}{*}{Gas Stations} & Video & & & & & & & \\  & & & Analytics & Y & & & & & & \\ \hline \multirow{2}{*}{Cities} & Traffic administration and & \multirow{2}{*}{\
\begin{tabular}{c} Intersections \\ and intelligent control \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} Video \\ and City \\ Clusters \\ \end{tabular} } & Video & & & & & & & & \\  & & & Analytics & & & & & & \\ \hline \multirow{2}{*}{Construction} & Increase safety, efficiency, and & Construction & Video & & Y & Y & & & \\  & productivity & Site & Analytics & & & & & & \\ \hline \multirow{2}{*}{Aviation} & Analyze customers’ in-flight & \multirow{2}{*}{Plane} & Video & & & & & & \\  & experience, monitoring and & & Analytics, & Y & Y & Y & Y & Y & \\  & maintenance of aircraft & & Operations & & & & & & \\ \hline \multirow{2}{*}{Railway} & Monitoring freight cars, train & \multirow{2}{*}{Train} & Video & & & & & & \\  & tracks, and wheels for issues & & Analytics & Y & Y & Y & Y & Y & Y \\ \cline{1-1}  & that could cause derailment & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Real-world deployment, use cases, and challenges of edge cloud applications [19; 20; 21]. The potential edge cloud applications can be found in different areas such as industrial IoT, restaurants, healthcare, retail, manufacturing, construction, transportation, agriculture, energy, and so on. It is used to aggregate, interact, filter, investigate, process, and analyze data at the boundary of the network. The primary driver behind edge computing is to enable IoT devices and IoT applications' capabilities with the aid of edge servers [13; 21]. As an example, restaurants can use predictive analytics to forecast food preparation, what is requirement now, and when more food needs to be made. IoT applications use ML- and AI-enabled  
analytics to foresee the quantity of clients and vehicles entering the store. So, the proclaimed essential objective of utilizing the edge is to make a trustworthy prediction. The customer is left pausing if the prediction fails (due to disconnecting) or takes excessively long (due to transferring a lot of data). To improve safety in the transportation system, the transportation sector is moving toward intelligent edge-based solutions. The edge cloud is utilized in a variety of modes of transportation such as ground, railway, and aviation [19].