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Artificial Intelligence (AI) and oneM2M IoT Architectures
In Traditional Programming, the Input Data and a Program are fed into a Machine to generate Output. When it comes to Machine Learning (ML), Input Data and Expected Output are fed into the Machine during the Learning Phase, and it works out a Program for itself. To understand this better, refer to the Figure below:
The flow of Data has an important bearing on Architectural Components for IoT Solutions, that is part of the analysis of the impact of AI on IoT Architectures, in particular the oneM2M Service Layer. The typical focus is on Data that leads to some form of decision being taken and is classified as 'User-Plane' (UP) Data. The basic Model for IoT Solutions begins with sourcing Data from IoT Devices (illustrated in left-hand side of Figure 1 below). This Data then passes through a Signal Processing and Machine Learning (ML) Process to extract key features and to represent them as Knowledge-based Objects. The next stage of processing involves the Application of Rules-based AI in areas related to Reasoning, Decision making, Supervision and Explainable AI.
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