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• W3081454690 abstract "With the ushering of Industry 4.0, IoT has seen a huge boost in wide spread adoption and commercialization. The amount of data generated by the IoT devices is gigantic in magnitude and is increasing due to their non-stop operational states. The sheer gigantic magnitude of this data disrupts predictable data processing and analytics functionality. However, cloud computing provides a solution for such data handling by offering services such as elastic computing, flexible storage, and backup compute engines for these IoT applications. Cloud provides services such as elastic computing, flexible storage, and backup compute engines for these IoT applications, but introduces the response delay, and hence degrades the overall performance of the network. Industry 4.0 contains many applications that require minimal delay for the real-time data processing. However, due to cloud delivery of applications, latency has been a significant issue. This introduces a need for localized data processing for such real-time applications. To overcome such issues, a new paradigm known as Fog Computing (FC) came into existence with the efficient cloud system complement features. It focuses on the deployment of micro clouds (fog nodes) near data sources (network edge). This chapter focuses on the process model that describes the process flow of data analytics using fog computing, and various modules that make up the fog computing architecture. We describe a use case of Fog Data Analytics (FDA) for healthcare IoT applications and concluded with current challenges of fog computing for better adoption of the technology." @default.
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• W3081454690 date "2020-01-01" @default.
• W3081454690 modified "2023-09-26" @default.
• W3081454690 title "Process Model for Fog Data Analytics for IoT Applications" @default.
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• W3081454690 doi "https://doi.org/10.1007/978-981-15-6044-6_9" @default.
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