An edge-fog-cloud platform for anticipatory learning process designed for Internet of Mobile Things

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This paper presents a novel architecture for data analytics targeting an anticipatory learning process in the context of the Internet of Mobile Things.

• The architecture is geo-distributed and composed by edge, fog, and cloud resources that operate collectively to support such an anticipatory learning process.
• We designed the architecture to manage large volumes of data streams coming from the IoMT devices, analyze in successive phases climbing up in the hierarchy of resources from edge, fog and cloud.
• We discuss the characteristics of the analytical tasks at each layer.