• Collaboratively administrate empowered markets via plug-and-play networks. 

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    Adding Knowledge to IoT

     

    IoT connectivity and Artificial Intelligence services provide almost unlimited new ways of capturing and analysing a wealth of structured & unstructured data. By using a single, common, knowledge automation tool as the brain of the data ecosystem, real business value can be achieved in the form of expert automated decisions.

     

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    In this sample

    This sample demonstrates the execution of XpertRule on both an edge device for local decisions (Raspberry Pi) & running in the cloud for augmented decisions (IBM Bluemix). The specific technologies include: 

    XpertRule Decision Author (Design Environment)
    XpertRule Node.js Runtime Environment (installed on a BlueMix Instance & Raspberry Pi)
    IBM Watson IoT Platform (for communication with IoT hardware)
    Raspberry Pi running IBM IoT client software
    IBM Alchemy Vision (to count number of people in an image)
    IBM Insights for Weather (for current weather conditions)

    RUN SAMPLE
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    Architecture

    The premise is to take IoT sensor values, along with face recognition and weather report, to control a fictional heating, ventilation & cooling (HVAC) system.

     sample3notextnew

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    Edge vs Cloud

    Advantages of running analytics at the edge:

    • Availability – If the connection to the cloud goes down, decisions can still be made
    • Scalability – Handling decisions for a large number of connected services in a timely manner requires a significant amount of resources.
    • Anomaly Reporting – Only report errors / anomalies back to the cloud.

    Advantages of running analytics in the cloud:

    • Global Decisions – Make decisions based on multiple connected devices
    • Enhanced Processing Power – Perform complex analytics (e.g. Image recognition, pattern matching…) which would be beyond the processing power available locally

     

     

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