IIoT Whitepaper

Although broader in scope, the Industrial Internet of Things (IIoT) builds on traditional control engineering and the Information Technology previously in place in manufacturing organizations. It can be thought of as a technology stack with integrated layers that increase in intelligence over four technology layers.


At the base-level of the system are the sensors, devices, and actuators that interface with production machinery on the factory floor. The sensors, devices, and actuators monitor and react to the various production events. An example of a device would be a vision system used to precisely align the materials used for making the product.

Generally, analog data is generated. This is fed to a production-line gateway located between the manufacturing line and a more powerful gateway deployed at the network’s edge one level up. The production-line gateway translates the analog to digal format and then refines it into more meaningful information. It provides human and M2M alerts when required; and stores appropriate volatile data. Processing at this level is sometimes referred to as fog computing, as it occurs before the network’s edge.

Machine-to-machine communications can be via physical or wireless link. Typical operations are monitoring the health of machines for predictive maintenance, detecting machine failure, monitoring logistic needs and ordering parts. In addition these gateways can facilitate the processing and disposition of faulty production output. Protocol translation from analog to digital and the ability to integrate RFID information with other data is considered essential for the production-line gateway. Data is collected and refined to its most meaningful content and passed to a higher-level edge server deployed at the network’s edge.

The edge gateway has higher-level analytical software. The edge gateway reports on data deemed valuable by management for increasing operational efficiency and lowering costs. The data is further refined to the essential and sent to a cloud-based server.

Data on the cloud server is viewed using a longer timeline, but content is generally limited to data considered by management to be essential and strategic for further lowering costs and improving manufacuring methods and efficiencies. It may incorporate the use of analytical techniques, such as mathematical modeling. Powerful processors and large storage capabilities are the principal contributions of the cloud-based server. These servers act as data repositories for the enterprises non-volatile, but pertinent operational data; and their powerful processors enable sophisticated analytics. 

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