Currently, the Internet of Things (IOT) platform used by large buildings to manage the indoor climate uses different controllers and sensors from multiple manufacturers. Communication between these devices requires a human in the loop to translate each devices data to to be compatible with a common integration engine and storage historian. The subject matter expert needs to decipher the non-standard naming convention used for each device and manually translate thousands of codes each time a new device has to be integrated. To aid the human translator, we propose a technique to implement a smart translator using Deep Neural Networks (DNN) by automatically assigning any registers with recognized data patterns to standardized labels.


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    Titel :

    IOT device code translators using LSTM networks


    Beteiligte:
    Cashion, Kelly (Autor:in) / Ravindran, Satish (Autor:in) / Powar, Nilesh (Autor:in) / Gold, Joshua (Autor:in)


    Erscheinungsdatum :

    2017-06-01


    Format / Umfang :

    478119 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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