Industrial Internet of Things (IIoT) Engineering is a discipline in and of itself — somewhat a macro-discipline. The IIoT engineer is focused on the expansion of the organization to a field-level mentality of acquisition, preparation, analysis, reaction, automation, assessment, etc..

IIoT vs ML vs AI vs Big Data vs Edge Computing

The Industrial Internet of Things (IIoT) is not synonym for Machine Learning (ML). Neither is it a synonym for Big Data. Neither is it a synonym for Artificial Intelligence (AI).

Generally, Machine Learning requires large amounts of data, aka Big Data. Machine Learning may exist within IIoT and utilize edge devices. But ML and AI operate on data, not devices. In fact, ML and AI should be utilizing data that has been prepared, cleaned, and verified — not on data directly out of a sensor.

The Range of the IIoT industry clearly includes Big Data, ML, and AI. However, IIoT enables ML and AI to assess a task, utilizes the results from ML and AI to optimize a task, but does not necessarily need ML and AI to accomplish a task.

Frequently, interconnecting IIoT edge-point devices via networks is often grouped into the narrower genre of edge computing. However, edge computers demands computational capabilities. 

IIoT edge-point apparatus do not necessarily need edge computing. Certainly within this age to digitization and digital transformation, these distinctions are somewhat immaterial. However, the distinction does exist.

IIoT edge-point in isolation is no longer relevant. As the lines of demarcation blur, we ask ourselves:

Why would a job employ a sensor unless that sensor is producing data that needs to be monitored and recorded?

If sensor devices are to be monitored and recorded, why do that job not utilize automated and autonomous acquisition, assessment, and archival? Once acquired, shouldn’t a project utilize that data to affect other processes, alert when anomalous situations exist, and be utilized in some way as a forecasting input?

The Industrial Internet of Things adds clarity that the items’ are industrial components, the world wide web is industrially focused, and the job is industrial in nature. I exclude the broader categories of IoT.

Communications involving the toaster and refrigerator are not, right, relevant to the Industrial Internet of Things.

The IIoT is different from other IoT applications in that it focuses on connecting machines and devices in sectors such as oil and gas, electricity utilities and healthcare.

Relevance to Industry

The relevant point to be made is that IIoT, Edge Computing, ML, and AI are tightly linked and often integrated. Regardless, disciplines focusing on IIoT, Edge ComputingMachine and Machine Learning require different skill-sets for the practitioners, different design methodology for integration, and different back-end infrastructure.

Engineering Skill-sets Required for IIoT Projects

Industrial Internet of Things is, of necessity, comprised of end-point devices such as sensors, annunciators, actuators, and terminals. As a practical nature, IIoT also has edge power, cabling, networks, and freedom. These skill are somewhat more focused toward the electrical engineer than other individual engineering disciplines (chemical, mechanical, etc.).

An electrical engineer must understand the capabilities of the individual devices, installation requirements of the devices, the environmental and mechanical limits of the devices, and the installation requirements of the devices. 

Additionally, maintainability, serviceability, reliability, and availability are crucial concerns.

The IIoT engineer ought to be fluent with the whole specification of the devices and components, able to design an integration of devices, and savvy in computers, networks, and security.

As a checklist, the IIoT Engineer should be demonstrably proficient in

Sensors, sensor technologies, sensor specifications, and sensor integration

User interfaces — mobile devices, operations centers, and field annunciators

Install-ability, Serviceability, Maintainability, Reliability, and Security

Units of Measure, Calibration, Sampling Rates, Filtering (hardware filters and software filters)

Data organization, storage, retrieval, and flow

This proficiency cannot exist in isolation from abilities in ML, AI, Big Data, and Analytics. Finally, the IIoT must be conversant with the world in which the IIoT have to exist — especially the nature of the operation, the worries of the field personnel, and the vernacular of the operation.

Design Methodology Required for IIoT Projects

An IIoT project is rarely finished. The first phase may come to an end, but one phase is rarely completed before the next phase starts. A design methodology is crucial.

The methodology selected for an IIoT endeavor should address

First and foremost: What is the objective? Why is an organization undertaking and IIoT job of any scale and sophistication? When finished, what benefit will be shown to the executive leadership?

Don’t eat the comprehensive elephant at a single sitting. Identify specific near-term, small, demonstrable, and achievable milestones. Early success will guarantee the embrace of leadership. 

However, nonsensical demonstrations of remote control lighting and lights will equally ensure the alienation of necessary backers.

Make sure the IIoT components are robust. In the intense, installation or wire-wrapped demonstrator devices will probably be prone to fail — at the wrong moment. Employ individual IIoT points, sensors, actuators, processors, gateways, etc together with the care to be given productionized solutions. Particularly appropriate is the environmental fitness of the installation (volatile environment, extreme temperature, caustic chemicals)

Address signal quality. Any electrical engineer may know that the signal to noise ratio must be of sufficient distinction to supply credible data (often an SN ratio of 3 or more is recommended). Additionally, ensure point source filtering is implemented to prevent spurious data, but maybe not so filtered to stop outliers.

Think about the end user. Great ideas have failed not because of the concept, devices, quality and robustness of the design, but because the user’s ability to consider the new information source is ill-conceived. 

Too much data and not enough information will sink a project. If the question is”have you been watching the display” , then the job has failed.

What behavior change is expected of the user or the machine based on the IIoT project?

Is the solution robust, maintainable, serviceable, and autonomous?

Past the premier consideration of these concepts, the design methodology should iterate and expand well.

Infrastructure Required for IIoT Projects

IIoT infrastructure will cross most of the organization. Specifically:

  • Field personnel to install, service, and calibrate the machine.
  • Communication is reliable and resilient such as the ability to back-fill data gaps after communication outage.
  • Data retention such as short-term raw quantity, long-term decimated quantity, behavior modelling and thumbprint, and KPI alerts.
  • Streaming data processing in 4 modalities: real-time monitoring, after-action or forensic look-back analysis, pre-action or workflow planning analysis, and large-field analytics and machine learning.
  • Geographic dispersion of IIoT devices, users, and programs.