The Industrial Internet of Things: A Primer

IIoT is an acronym for the “Industrial Internet of Things,” referring to connected devices and advanced analytics in manufacturing, transportation, energy, and other industries.

General Electric coined the term “Industrial Internet” in 2012 to describe a third wave of innovation (the first being the Industrial Revolution of the 19th century and the second being the introduction of mainframe computers in the 1950s through the 1990s.) Today, industrial internet represents the fourth industrial revolution, or Industry 4.0, which brings together smart devices, advanced data systems, and physical and human networks to allow businesses to increase operational efficiencies and improve business decisions and outcomes.

IIoT is part of a larger system of interconnected devices called IoT, or the “Internet of Things.” In IoT, a “thing” could be any physical object from a coffee pot to a car, as long as it can connect to the internet and communicate with a network of other devices without human intervention.

The benefits of IIoT are numerous, and it has multiple applications across a wide range of industries. Connected companies see a reduction in downtime and maintenance costs, an increase in production, stronger workplace safety, and generally get a better idea of what’s going on in their operations. We’ll explore all of these ideas, including how IIoT is being used in digital transformation, in the pages that follow.

IIoT Overview

IIoT technology is used to bring automated instrumentation, data collection and analysis, reporting and decision making to industrial operations. It enables this through an interconnected system of smart sensors, gateways, software platforms, and cloud servers. Sensors are deployed on machines where they capture data and send it to the gateway, which functions as a hub between the connected devices and applications and services running in the datacenter or the cloud.. This data can then be accessed by workers via a computer or mobile device.

How IIoT is used in practice varies widely, with new industrial applications being introduced as more industries adopt it. Several applications have proven particularly popular, including:

IIoT has spurred innovations in manufacturing, farming, transportation, and energy management and further developments are expected in other industries over the next several years.

How is IIoT used?

IIoT vs IoT: What's The Difference?

In the broadest terms, the difference between IIoT and IoT is their respective purpose. IIoT’s focus is to improve an array of industrial processes, while IoT is mainly concerned with increasing consumer convenience. By looking closely at how each achieves its goal, we can get a more detailed picture of how they differ.

IIoT is unique from other IoT technologies because it is tailored to industrial requirements. Among other things, IIoT devices have to be extremely reliable. A light-rail train, electrical grid, or even an airline baggage tracker that goes offline entails significant consequences. IIoT devices are engineered to maintain connectivity and have long lifespans, while also securing data in transit and at rest.

Additionally, a device deployed in an industrial setting needs the ability to integrate with various business systems, such as ERP, EAM, CMMS and others, while also interacting with hundreds of people in a single day. It also needs to take into account a variety of protocols and data formats, making data analysis challenging in light of traditional management technologies. That means that it must be able to communicate frequently, include different applications for each of its functional roles, and recall varying access privileges.

Finally, IIoT is an investment that can help facilitate stronger and more strategic business decisions. Organizations that implement IIoT systems require ROI in the form of reduced maintenance costs, increased efficiency, and improved productivity.

IIoT in Manufacturing

The manufacturing industry has been one of the most enthusiastic adopters of IIoT, largely because the technology enables a host of efficiencies for the industry. With IIoT, equipment can be managed remotely, monitored in real-time, and proactively maintained. The condition, location, and status of products are much easier to track. Product usage patterns are also more easily identified with IIoT, allowing manufacturers to increase production of items that are popular and discontinue those that aren’t before they negatively impacts the organization.

IIoT Manufacturing

Here are some of the most popular IIoT use cases in today's manufacturing industry:

How IIoT works with Manufacturing Execution Systems

The question of whether IIoT will replace or complement MES (Manufacturing Execution Systems) is still being debated. Some insiders argue that IIoT will eventually displace MES or force it to modernize, maintaining that most of these systems are outdated and aren’t equipped to collect data in real time from sensors deployed on the factory floor. Nor were they designed for long-term data storage, AI, and analytics. This results in data that’s siloed, if it’s collected at all, and hinders a factory’s ability to achieve a comprehensive view of its operations, predict mechanical failure and other disruptions, and optimize processes.

Others make the case that the two systems are complementary. One of the arguments is that MES can provide product and maintenance data that allows IIoT to predict failure. In this scenario, MES acts as a proxy for devices devoid of sensors, communicating with the IIoT system on behalf of those devices. MES can map and store information on operations that allows IIoT to operate a facility autonomously.

While the question is far from settled, manufacturers wanting to adopt IIoT solutions will have to rethink how they use their MES in the short term.

IIoT as a part of Industry 4.0

IIoT is one of the critical components of Industry 4.0.

Industry 4.0 is a term referring to the technological advancements and new approaches adopted in the industrial sector over the last decade. This period has been informally christened “the fourth revolution” in manufacturing. The first was the mechanization of manufacturing processes through water and steam power. The second was the introduction of assembly lines and the advent of electricity. The third revolution was the rise of computers and introduction of automation to industrial processes. And the fourth industrial revolution is defined by the integration of technologies and new processes — such as IIoT, Cyber-Physical Systems (CPS), Cognitive Computing (CC), and Machine-to-Machine communication (M2M) — into industrial infrastructures.

Defining the IIoT Platform

An IIoT platform is a set of hardware and software that work together to connect industrial processes with information systems. This middle layer can include a variety of components, but at a minimum, it includes the base software (often SaaS), IoT devices, and physical gateways that connect the two together.

Both the software and hardware components are made up of many individual elements. Hardware typically includes smart sensors, IoT devices, human-machine interfaces (HMIs), edge devices, and industrial machinery. Software encompasses an operating system, runtime system, cloud-based software, app development environment, data visualization and data storage tools. There are also industry-specific IIoT platforms and industrial applications that are designed for industries like rail or utility companies.

An IIoT platform’s main purpose is to give you centralized control of all your connected machines and processes while providing the means to view your entire operation and glean the insights to optimize as conditions and requirements change.

The Bottom Line

IIoT is the future of industrial business

IIoT is one of the most significant trends for business processes in industrial environments. And as market speed increases and technology disruptions become more frequent, IIoT is helping companies stay agile and competitive.

IIoT improves just about every aspect of industrial operations: cost savings through predictive maintenance, new operational efficiency through automation, sustainable energy consumption and increased productivity through safety improvements. Further, IIoT gives you previously unimaginable visibility into your enterprise with centralized control and aggregated data analysis. In short, IIoT can enable new levels of performance and profitability in any industrial setting.

FAQs about Industrial Internet of Things (IIoT)

What is the Industrial Internet of Things (IIoT)?
The Industrial Internet of Things (IIoT) refers to the use of connected devices, sensors, and software in industrial settings to collect, exchange, and analyze data for improved efficiency, productivity, and safety.
How does IIoT differ from IoT?
IIoT focuses specifically on industrial environments such as manufacturing, energy, and transportation, while IoT is a broader term that includes consumer devices and applications.
What are some benefits of IIoT?
Benefits of IIoT include increased operational efficiency, predictive maintenance, reduced downtime, improved safety, and better decision-making through data-driven insights.
What industries use IIoT?
Industries that use IIoT include manufacturing, energy, utilities, transportation, oil and gas, and logistics.
What are the challenges of implementing IIoT?
Challenges of implementing IIoT include data security, integration with legacy systems, scalability, and managing large volumes of data.

Related Articles

The Bulkhead and Sidecar Design Patterns for Microservices & Incident Resolution
Learn
3 Minute Read

The Bulkhead and Sidecar Design Patterns for Microservices & Incident Resolution

This article looks at Bulkhead and Sidecar design patterns, including how they’re used in microservice designs — and how they help overall incident support.
Content Delivery Networks (CDNs) vs. Load Balancers: What’s The Difference?
Learn
3 Minute Read

Content Delivery Networks (CDNs) vs. Load Balancers: What’s The Difference?

CDNs and load balancers fulfill similar roles, but they are different tools. This article breaks down the differences so you can decide which is right for you.
Best DevOps Books: The Definitive List
Learn
4 Minute Read

Best DevOps Books: The Definitive List

In this blog post we’ll look at the core, fundamental books that have played the largest role in creating the modern DevOps movement.
Kubernetes 101: How To Set Up “Vanilla” Kubernetes
Learn
4 Minute Read

Kubernetes 101: How To Set Up “Vanilla” Kubernetes

Kubernetes 101: Set up the most basic K8s cluster — also known as Vanilla Kubernetes — with this hands-on tutorial that gets you started quickly and easily.
Network vs. Application Performance Monitoring: What's The Difference?
Learn
5 Minute Read

Network vs. Application Performance Monitoring: What's The Difference?

Monitoring networks and application performance are different practices. Understand the changes and see how, together, both can offer end-to-end observability.
Monitoring Windows Infrastructure: Tools, Apps, Metrics & Best Practices
Learn
3 Minute Read

Monitoring Windows Infrastructure: Tools, Apps, Metrics & Best Practices

Learn how to monitor your Windows infrastructure, including the best tools and apps to use, the top metrics to monitor and how to analyze those metrics.
NoOps Explained: How Does NoOps Compare with DevOps?
Learn
5 Minute Read

NoOps Explained: How Does NoOps Compare with DevOps?

Take a look at NoOps, the concept of automating IT and development: how it works, pros and cons and whether it’s an evolution — or the end — of DevOps.
How To Prepare for a Site Reliability Engineer (SRE) Interview
Learn
4 Minute Read

How To Prepare for a Site Reliability Engineer (SRE) Interview

Prepare for your SRE interviews. These are common questions and answers to expect in any site reliability engineer interview.
Adaptive Thresholding with Splunk's Density Function
Learn
3 Minute Read

Adaptive Thresholding with Splunk's Density Function

Past data supports adaptive thresholding with Splunk. Learn how — and when — to use the probability density function to create adaptive thresholding.