Edge Gateway 

The Edge Gateway is in close proximity to the sensors and actuators and it is usually located on a customer site or on the equipment itself. For example, a wind turbine might contain a hundreds sensors and actuators that feed data into an Edge Gateway. The Edge Gateway then collects, digitizes, and forwards the data to the cloud and, sometimes, may also have edge analytics which can take detect patterns and anomalies at the edge itself. Edge Gateways usually support various types of protocols, such as Modbus and OPCUA to connect with the various types of industrial sensors, as can be seen in the following diagram:

Edge Gateway can also pre-process data by applying functions for filtering and resampling of data. The data generated by sensors creates large volumes of data rapidly and, by pre-possessing the data, the gateway can only send relevant data to cloud. Analog sensors, such as temperature, voltage, pressure, and vibration can generate huge volumes of continuous streams of data that are changing rapidly. As an example, a jet engine can have thousands of sensors, and these sensors can generate ~40 TB of data during aircraft takeoff or landing alone. Pre-processing data on the Edge is therefore beneficial in many cases.

Edge Gateways can build basic gateway functionality by adding such capabilities as security, analytics, malware protection, and data management services, which are collectively known as fog computing. Fog computing enables the analysis of data streams in near realtime by running machine learning algorithms directly onto the Edge Gateway. The Edge Gateway is the perfect junction where there are enough compute, storage and networking resources to mimic cloud capabilities at the edge, and hence support the local ingestion of data and the quick turnaround of results:

Edge Gateways are edge devices that are deployed on customer premises or customer data centers. In the wind turbine example, if you have a wind farm with many hundreds of turbines and you want to process data on-premises, you might have instant data at each turbine. You would then aggregate the information to create a wind farm wide view and pass the data on to the cloud for a company-wide view. Edge Gateway device hardware footprints are very diverse and supported in various form factors to support specific deployment needs. Most of the manufacturers will support many different specifications, all the way from onboard gateways to specialized servers called Edge Nodes. All of them have typically common characteristics, such as ease of deployment, being rugged, and the ability to support remote management of the devices using edge managers.

Because IIoT data is voluminous, it can easily consume large amounts of network bandwidth, it needs huge storage space and can swamp the resources in Cloud. It's optimal to intelligent Edge Gateways which are capable of performing analytics as a way to get immediate insights. Alternatively, we can apply different time-based filtering such as a Bloom filter. Adding this type of filtering can reduce the resource needs and hence it can be much more efficient. By adding an intelligent Edge Gateway, or Edge Node, we can pre-process the data, run appropriate Edge Analytics to gain immediate insights, and then pass data to the cloud for further processing and correlations across multiple sites, and so on. For example, a power company may have multiple wind farms, and processing in each of these individual farms can happen at the site using the Edge Gateway or Nodes. In addition, these Nodes can send relevant data to the cloud for further processing to gain insights and comparisons between farms.

 Edge Analytics is an up-and-coming field, whereby instead of sending the data to the cloud for processing and gaining insights, data is processed near the source on the Edge Gateway or Node. For example, machine learning algorithms, such as anomaly detection, can be applied to sensor data to scan for anomalies that can identify many different maintenance problems of the machines, thereby preventing any downtime, as long appropriate action is taken. In the next section, we will cover different ways to connect to the cloud to send data to the cloud.