# An Overview of Fog Data Analytics for IoT Applications
## 5 Research Challenges
### Scalability  
Scalability is among the main characteristics, the need of which gave birth to cloud capabilities near the end devices so that, instead of overwhelming the cloud with a huge amount of data, fog focuses on filtering it at the fog layer. By 2022, USD 2.5 million will be spent every minute on the IoT, and one million new IoT devices will be sold every hour. Now, processing at the edge would help to overcome the problem more efficiently at this scale. However, not only computing at the edge but also the coordination of such a large number of devices needs to be handled as requirements grow over time. This presents the challenge of designing a scalable solution to accommodate the data analytics needs of collected data from connected devices. A variety of use cases have different approaches for solving the particular problem, and they might be inherently disparate, serving those specific requirements. For example, for the management of smart home devices, one fog node equipped with sufficient resources might be enough. However, regarding connected devices throughout the city, a large number of fog nodes is certainly required. In addition, the resources need to be scaled with the increased number of users. The relevance of placing services at various levels of the tree topology, which is rooted at the cloud data center, is discussed in [59] to optimize the performance and scalability. The work [70] presented multi-tier fog architecture consisting of ad hoc and dedicated nodes with dedicated and opportunistic computing resources, primarily focusing on maximizing analytics service utilities. The authors in [71; 72] proposed systems that support real-time efficient data processing for vehicular fog networks and easy traffic management. \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Work** & **Scope** & \begin{tabular}{c} **Quality of** \\ **Experience** \\ \end{tabular} & \begin{tabular}{c} **Energy** \\ **Efficiency** \\ \end{tabular} & \begin{tabular}{c} **Delay** \\ **Sensitiveness** \\ \end{tabular} & **Reliability** & \begin{tabular}{c} **In-Network** \\ **Caching** \\ \end{tabular} \\ \hline [PERSON] et al. [68] & \begin{tabular}{c} Machine-to-machine \\ networks \\ \end{tabular} & ✓ & & ✓ & & \\ \hline [PERSON] et al. [66] & Vehicular networks & ✓ & & ✓ & & \\ \hline [PERSON] et al. [73] & e-health applications & ✓ & & ✓ & & \\ \hline [PERSON] et al. [74] & \begin{tabular}{c} Smartphone-based \\ applications \\ \end{tabular} & ✓ & & ✓ & & \\ \hline [PERSON] et al. [75] & IoT-based applications & & ✓ & ✓ & & \\ \hline [PERSON] et al. [76] & IoT-based applications & ✓ & ✓ & ✓ & & \\ \hline [PERSON] et al. [77] & Website rendering & & & ✓ & & ✓ \\ \hline [PERSON] et al. [78] & Mobile applications & ✓ & ✓ & ✓ & & \\ \hline [PERSON] et al. [79] & Ubiquitous computing & ✓ & ✓ & & ✓ \\ \hline [PERSON] et al. [80] & Automation applications & & ✓ & & \\ \hline [PERSON] et al. [81] & Radio access networks & ✓ & ✓ & ✓ & \\ \hline [PERSON] et al. [69] & IoT-based applications & ✓ & ✓ & ✓ & \\ \hline [PERSON] et al. [82] & \begin{tabular}{c} Social-aware device-to- \\ device communication \\ \end{tabular} & & & ✓ & & \\ \hline [PERSON] et al. [83] & Web-based applications & & ✓ & & ✓ \\ \hline [PERSON] et al. [54] & \
\begin{tabular}{c} Device-to-device \\ communications \\ \end{tabular} & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline [PERSON] et al. [65] &
\begin{tabular}{c} Latency-sensitive \\ applications \\ \end{tabular} & ✓ & ✓ & & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of QoS aspects from various works. Not only the placement and efficiency of fog nodes but also the number of fog nodes and type are the factors affecting the network's performance. It is also a necessity for the placement of nodes to be flexible according to the demand at a certain time. In the case of a smart city, fog placement should be dynamic enough to support changing the number of users at any time and to continue operating and servicing users. To support this goal, ref.