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Review
. 2022 Dec 24;23(1):199.
doi: 10.3390/s23010199.

An Overview of Fog Data Analytics for IoT Applications

Jitendra Bhatia  1 , Kiran Italiya  2 , Kuldeepsinh Jadeja  3 , Malaram Kumhar  1 , Uttam Chauhan  4 , Sudeep Tanwar  1 , Madhuri Bhavsar  1 , Ravi Sharma  5 , Daniela Lucia Manea  6 , Marina Verdes  7 , Maria Simona Raboaca  6   8
Affiliations
Review

An Overview of Fog Data Analytics for IoT Applications

Jitendra Bhatia et al. Sensors (Basel). .

Abstract

With the rapid growth in the data and processing over the cloud, it has become easier to access those data. On the other hand, it poses many technical and security challenges to the users of those provisions. Fog computing makes these technical issues manageable to some extent. Fog computing is one of the promising solutions for handling the big data produced by the IoT, which are often security-critical and time-sensitive. Massive IoT data analytics by a fog computing structure is emerging and requires extensive research for more proficient knowledge and smart decisions. Though an advancement in big data analytics is taking place, it does not consider fog data analytics. However, there are many challenges, including heterogeneity, security, accessibility, resource sharing, network communication overhead, the real-time data processing of complex data, etc. This paper explores various research challenges and their solution using the next-generation fog data analytics and IoT networks. We also performed an experimental analysis based on fog computing and cloud architecture. The result shows that fog computing outperforms the cloud in terms of network utilization and latency. Finally, the paper is concluded with future trends.

Keywords: Internet of things; artificial intelligence; blockchain; data analytics; fog computing.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Industrial revolution.
Figure 2
Figure 2
Layered architecture with fog computing.
Figure 3
Figure 3
Architecture of fog node.
Figure 4
Figure 4
Data generation sources.
Figure 5
Figure 5
Flow diagram for data analysis.
Figure 6
Figure 6
Data analytics using fog computing.
Figure 7
Figure 7
Fog network of heterogeneous devices.
Figure 8
Figure 8
Fog data analytics in healthcare.
Figure 9
Figure 9
Latency.
Figure 10
Figure 10
Network utilization.
Figure 11
Figure 11
Request service ratio.
See this image and copyright information in PMC

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