Part 4 – Fog and edge compute, analytics, and machine learning

At this point, we must consider what to do with the data streaming in from edge nodes into a cloud service. First, we begin by talking about the aspects of cloud architectures such as SaaS, IaaS, and PaaS systems. An architect needs to understand the data flow and typical design of cloud services (what they are and how they are used). We use OpenStack as a model of cloud design and explore the various components from ingestor engines, to data lakes, to analytics engines. Understanding the constraints of cloud architectures is also important to make a good judgment on how a system will deploy and scale. An architect must also understand how latency can affect an IoT system. Alternatively, not everything belongs in the cloud. There is a measurable cost in moving all IoT data to a cloud versus processing it at the edge (edge processing), or extending cloud services downward into an edge router (fog computing). The section pes deep into new standards of fog compute, such as the OpenFog architecture.  

Data that has been transformed from a physical analog event to a digital signal may have actionable consequences. This is where the analytics and rules engines of the IoT come in to play. The level of sophistication for an IoT deployment is dependent on the solution being architected. In some situations, a simple rules engine looking for anomalous temperature extremes can easily be deployed on an edge router monitoring several sensors. In other situations, a massive amount of structured and unstructured data may be streaming in real time to a cloud-based data lake, and require both fast processing for predictive analytics, and long-range forecasting using advanced machine learning models, such as recurrent neural networks in a time-correlated signal analysis package. This chapter details the uses and constraints of analytics from complex event processors, to Bayesian networks, to the inference and training of neural networks.