IEEE Internet of Things Journal · 2022 · 120 citations · 99 references
ComputingCloud ComputingHealth InformaticsCloud ArchitectureEdge ComputingInternet Of ThingsEdge ArchitectureEdge-cloud ArchitectureEdge-cloud IntegrationFog ComputingIot Edge ComputingDigital WorldsEdge-to-cloud ContinuumFog ArchitectureFog Computing ArchitectureFog-cloud CollaborationInternet Of Things Edge ComputingReference ModelsTechnical Reference Models
The digital transformation is characterized by the convergence of technologies—from the Internet of Things (IoT) to edge–fog–cloud computing, artificial intelligence (AI), and Blockchain—in multiple dimensions, blurring the lines between the physical and digital worlds. Although these innovations have evolved independently over time, they are increasingly becoming more intertwined, driving the development of new business models. With more adaptation, embracement, and development, we are witnessing a steady convergence and fusion of these technologies resulting in an unprecedented paradigm shift that is expected to disrupt and reshape the next-generation systems in vertical domains in a way that the capabilities of the technologies are aligned in the best possible way to complement each other. Despite the fact that the convergence of the four technologies can potentially tackle the main shortcomings of the existing systems, its adoption is still in its infancy phase, suffering from several issues, such as the absence of consensus toward any reference models or best practices. This article provides a comprehensive insight into the fusions of these paradigms by discussing a blend of topics addressing all the importation aspects from design to deployment. We will begin this article by providing an in-depth discussion on the main requirements, state-of-the-art reference architectures, applications, and challenges. Following this, we will present a reference architecture and a case study on privacy-preserving stress monitoring and management to better elaborate on the corresponding details and considerations.
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Edge Computing: Vision and Challenges
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Deep Learning with Differential Privacy
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