Data Science for Internet of Things – The Big Picture

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      This big picture view lays the foundation of our book Data Science for the Internet of Things. (Co-authored by Ajit Jaokar, Jean Jacques Bernard and Sukanya Mandal)

      We address the question: at what points can we add analytics to the data after it leaves the sensor and what are the implications of doing so at various stages.

      In this diagram, we present the big picture through two process flows:

       

      Technology flow: Edge to Stream to Store
      Deployment flow: Model build, deploy and refresh in production including at the edge
      Data Science for IoT implementation differs from traditional Data Science in four key aspects

      Edge Computing
      Feature Engineering for IoT
      Complex event processing
      Embedded AI
      The last three are not shown in the diagram to make it more readable.

       

       

      Notes:

      From an IoT analytics perspective, Mobile devices could also function as Edge devices.
      AI when deployed at the edge works primarily for inference(currently)
      Most Data Science for IoT problems are time series problems. In practise, this means the use of LSTMs in many cases. We could also work with images, sound etc (using convolutional neural networks etc)
      The diagram does not show batch mode of processing which exists in many applications
      In terms of deployment models, we consider three options:

      Digital signals: for example, in insurance where IoT provides a signal to enhance existing business processes
      Digital twin: Mainly in industrial iot
      Digital wand: A ‘pervasive’ deployment of AI / ubiquitous computing. Currently, seen in some smart city applications in China


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