Fundamentals: The IoT, IIoT, AIoT, and Why They’re the Future of Industrial Automation

By Clive "Max" Maxfield

Contributed By Digi-Key's North American Editors

As the rate of adoption of the Internet of Things (IoT) accelerates, there is also a pull for more advanced technologies such as artificial intelligence (AI) and machine learning (ML). So much so, that the meaning of the term “IoT” is itself evolving and expanding into the Industrial IoT (IIoT), the Artificial Intelligence of Things (AIoT), and the Internet of Heavier Things1.

In the case of industrial deployments, the connectivity and intelligence afforded by the IIoT offers productivity, efficiency, and other economic benefits. However, in addition to new IIoT-ready equipment, there is a large amount of existing "dumb" (legacy) infrastructure and machinery.

Instead of letting this equipment deteriorate on the outskirts of technological innovation, this article will show how facilities managers have the incentives and means to incorporate this legacy equipment into the era of the IIoT with solutions from Molex, TE Connectivity, STMicroelectronics, Delta, and Weidmuller.

Definition of terms

The term “Internet of Things” was coined by British technology pioneer Kevin Ashton during a presentation he made at Procter & Gamble (P&G) in 1999. Kevin used “Internet of Things” to describe a system where the internet is connected to the physical world via ubiquitous sensors. It wasn’t long before the term Internet of Things and its IoT abbreviation had themselves become ubiquitous.

The IoT: What people understand by the term "Internet of Things (IoT)" has evolved over time. The widely accepted current definition is: “A system of interrelated computing devices, mechanical and digital machines, objects, animals, or people that are provided with unique identifiers and the ability to transfer data over a network without necessarily requiring human-to-human or human-to-computer interaction.” Meanwhile, the term "IoT device" refers to any standalone internet-connected device that can be monitored and/or controlled from a remote location. According to Statistica, there are expected to be ~30 billion IoT devices installed around the world in 2020, rising to ~75 billion in 2025.

The IIoT and AIoT: The industrial internet of things (IIoT) refers to interconnected sensors, instruments, and other devices networked together with computers' industrial applications, including manufacturing and energy management. This connectivity allows for data collection, exchange, and analysis, potentially facilitating improvements in productivity and efficiency as well as other economic benefits. The IIoT is an evolution of a distributed control system (DCS) that allows for a higher degree of automation by using cloud computing to refine and optimize the process controls. The IIoT in its current form is supported by technologies such as cybersecurity, cloud computing, edge computing, mobile technologies, machine-to-machine, 3D printing, advanced robotics, big data, the IoT, RFID technology, and cognitive computing.

The AIoT refers to augmenting IoT devices and infrastructure with AI technologies. The AI augments the IoT with machine learning (ML) and cognitive capabilities.

The Industrial Awakening and the Internet of Heavier Things

According to a 2017 prediction by Gartner, global spending on the IoT was expected to reach $772.5 billion in 2018. Meanwhile, according to IDC, in 2018, global consumer spending on the IoT was around $62 billion. By comparison, manufacturing spent $189 billion, outstripping transportation ($85 billion) and utilities ($73 billion) combined. Furthermore, Bain & Company predicts that IIoT applications will generate more than $300 billion by 2020, double that of the consumer IoT segment ($150 billion).

The term “Heavy Industry” refers to an industry that involves one or more characteristics, such as large and heavy products, large and heavy equipment and facilities (e.g., heavy equipment, large machine tools, huge buildings, and large-scale infrastructure), or complex or numerous processes.

Prior to the IoT, industrial systems using motors, generators, and heavy machinery were largely unconnected and ran in isolation. However, there are tremendous advantages to be gained in terms of efficiency, productivity, and reliability from being connected to the internet and becoming part of the IoT. These advantages include capabilities such as remote monitoring and control, fault detection and pre-emptive maintenance. This explains why new industrial equipment comes equipped with a tremendous range of sensors and communications functions.

The problem is that there is a large amount of existing (legacy) "dumb" infrastructure and machinery. It is estimated that there is $6.8 trillion worth of such equipment in the USA alone. The choices are to stay as-is, replace existing equipment with modern equivalents at tremendous cost, or augment and enhance existing equipment with modern sensor, control, and communications systems and drag it kicking and screaming into the 21st century.

American venture capital firm Kleiner Perkins has designated the augmenting of industrial systems with IIoT and AIoT capabilities as the "Industrial Awakening." In an article published in 2015, The Industrial Awakening: The Internet of Heavier Things, Kleiner Perkins referenced a report generated by the World Economic Forum, which noted that this "Industrial Awakening" is expected to generate $14.2 trillion of global output by 2030.

Augmenting legacy equipment with IIoT and AIoT capabilities

Electric motors are the single biggest consumer of electricity around the globe. They account for around 2/3 of industrial power consumption and around 50% of global power consumption. This means that every second power plant or other power source is used only for powering motors.

The problem is that the average industrial motor is only around 88% efficient (commercial motors can be substantially less). This efficiency can be dramatically improved by means of appropriate sensors and control systems.

One of the biggest risks to an industrial enterprise is downtime caused by unexpected equipment failure. One way to mitigate this problem is to employ predictive maintenance practices, which involves using sensors to monitor the equipment and IIoT and AIoT capabilities to detect any deviations from normal operation and predict potential failure modes and time frames (e.g., "This secondary rotator on this machine is currently operating at 95% efficiency, decreasing by 0.9% a day, and is expected to fail catastrophically in 6 days +/- 1 day").

The reason for using IIoT and AIoT capabilities is that they can spot patterns, extract trends from historical data, and extrapolate potential failures far more effectively than human beings.

Humans find it hard to detect patterns and identify anomalies when presented with vast amounts of numerical data, but they find it much easier to detect patterns and identify anomalies when that data is presented in graphical form.

For example, it would be difficult, if not impossible, for a human to detect and identify the problem in the numerical data presented in Figure 1. By comparison, when the same data is presented in a graphical manner, a human would immediately spot the anomaly, as illustrated in Figure 2.

Image of generic measurements from an IoT system sanitized for public presentation (click to enlarge)Figure 1: Humans find it hard to detect patterns and identify anomalies when presented with vast amounts of numerical data. (Image source: "Generic measurements from an IoT system sanitized for public presentation" from a presentation by Stephen Bates)

Image of data presented in graphical formFigure 2: Humans find it much easier to detect patterns and identify anomalies when presented with data presented in graphical form. (Image source: "Generic measurements from an IoT system sanitized for public presentation" from a presentation by Stephen Bates)

The point here is that IIoT and AIoT systems can detect patterns and identify anomalies irrespective of how the data is presented. Furthermore, when multiple identical systems—potentially in disparate locations scattered around the world—are all being monitored, the IIoT and AIoT systems can learn from all of them and use the knowledge from one to predict problems in another.

It's all about the sensors (and processing and connectivity and...)

The first step in augmenting legacy industrial equipment is to add sensors. There is a tremendous variety of different types of sensors; also, there is a tremendous range of options for each sensor type. The various properties that sensors can measure include, but are not limited to, the following:

  • Position
  • Motion
  • Velocity and acceleration
  • Force (tactile and threshold)
  • Pressure (force per unit)
  • Flow (rate and volume)
  • Sound
  • Light
  • Radiation
  • Humidity (absolute and relative)
  • Temperature
  • Chemical (type, concentration, etc.)

There are literally tens of thousands of different sensor type/option combinations. A few examples include the Contrinex 120254 series of photoelectric sensors from Molex and the M3041-000006-250PG vented gauge pressure from TE Connectivity Measurement Specialties (Figure 3). The M3041-000006-250PG is part of the Microfused line from TE Connectivity and is suitable for measurement of liquid or gas pressure, even for difficult media such as contaminated water, steam, and mildly corrosive fluids.

Image of TE Connectivity M3041-000006-250PG pressure transducerFigure 3: The M3041-000006-250PG pressure transducer is suitable for measurement of liquid or gas pressure, even for difficult media such as contaminated water, steam, and mildly corrosive fluids. (Image source: TE Connectivity)

Some examples of sensor development kits and evaluation boards are the IoT Studio Platforms, the STEVAL-STLCS02V1 SensorTile, the STEVAL-MKSBOX1V1 Development Kit, and the X-NUCLEO-IKS01A3 Motion MEMS Evaluation Board, all from STMicroelectronics.

The X-NUCLEO-IKS01A3 motion MEMS and environmental sensor evaluation board system is compatible with the Arduino UNO R3 connector layout (Figure 4). It features the LSM6DSO 3-axis accelerometer + 3-axis gyroscope, the LIS2MDL 3-axis magnetometer, the LIS2DW12 3-axis accelerometer, the HTS221 humidity and temperature sensor, the LPS22HH pressure sensor, and the STTS751 temperature sensor.

Image of STMicroelectronics X-NUCLEO-IKS01A3 motion MEMS and environmental sensor evaluation boardFigure 4: The X-NUCLEO-IKS01A3 motion MEMS and environmental sensor evaluation board system is compatible with the Arduino UNO R3 connector. (Image source: STMicroelectronics)

In addition to the sensors, local data conditioning, processing, and control will be required. These tasks can be performed using programmable logic controllers (PLCs), such as the AS Series compact modular mid-range PLC from Delta Industrial Automation (Figure 5).

The AS Series is a high performance multi-purpose controller designed for all kinds of automated equipment. It features Delta's self-developed system-on-chip (SoC) based on 32-bit CPUs for enhanced execution speed up to 40 kilo-steps per millisecond. It supports up to 32 extension modules or up to 1,024 inputs/outputs.

Image of Delta Compact Modular Mid-range PLC AS SeriesFigure 5: The Delta Compact Modular Mid-range PLC AS Series supports up to 40 kilo-steps/ms and up to 1,025 inputs/outputs. (Image source: Delta Industrial Automation)

Meanwhile, advanced AIoT-based analytics will take place in the fog and the cloud, which will require networking and communications, such as the Complete Solution for Industrial Ethernet Connectivity from the Weidmuller Group.


As the rate of adoption of the IoT accelerates, and ML and AI are added, facilities managers need to find a way to modernize legacy industrial equipment accordingly to enhance productivity and efficiency.

Fortunately, there are readily available solutions from multiple vendors that can add intelligence and connectivity to legacy systems to make them part of the IIoT revolution.


  1. The Industrial Awakening: The Internet of Heavier Things, Kleiner Perkins, 2015

Disclaimer: The opinions, beliefs, and viewpoints expressed by the various authors and/or forum participants on this website do not necessarily reflect the opinions, beliefs, and viewpoints of Digi-Key Electronics or official policies of Digi-Key Electronics.

About this author

Clive "Max" Maxfield

Clive "Max" Maxfield received his BSc in Control Engineering in 1980 from Sheffield Hallam University, England and began his career as a designer of central processing units (CPUs) for mainframe computers. Over the years, Max has designed everything from silicon chips to circuit boards and from brainwave amplifiers to steampunk Prognostication Engines (don't ask). He has also been at the forefront of Electronic Design Automation (EDA) for more than 30 years.

Max is the author and/or co-author of a number of books, including Designus Maximus Unleashed (banned in Alabama), Bebop to the Boolean Boogie (An Unconventional Guide to Electronics), EDA: Where Electronics Begins, FPGAs: Instant Access, and How Computers Do Math. Check out his “Max’s Cool Beans” blog.

About this publisher

Digi-Key's North American Editors