Industrial Internet of Things (IIoT): Technologies and Infrastructures

The use of wireless technology has changed everything. It has inevitably resulted in the emergence of the Internet of Things (IoT). What? What is this “IoT”?

The Internet of Things (IoT) is a paradigm that refers to the interconnection of everyday objects called things such as home appliances, automotive, smart thermostats, and others to the Internet in order to collect, store, analyze, compute, and exchange the data with each other with least human intervention.

The data is collected via. IoT devices are called nodes and are popularly categorized as sensors and actuators (or IoT hardware), among many others.

Sensors gather the needful data and transmit it over the Internet, whereas the actuators perform the necessary action based on the decision governed by the processed data. These sensory devices are connected to the Internet via. Bluetooth, cellular data, Zigbee, and so forth.

Thus, IoT has evolved enough to provide cutting-edge means for efficient data analysis. It enhances the rate of productivity, optimizes performance and efficiency, expedites decision-making, and creates a plethora of business opportunities for endless growth.

It has helped devices become smarter, more connected, and more interactive in a profusely meaningful manner than ever before.

Industrial Internet of Things (IIoT)

IoT is mainly designed to connect things such as home appliances, wearables, home automation, and so forth to the Internet, whereas IIoT (stands for industrial Internet of things) emphasizes medium to heavy industrial machinery such as mills, drill presses, planers as well as steam boilers, turbomachinery, automobiles, production assembly and final packaging lines among many others in this regard (figure below).

industrial consumer iiot

When we look back into history, we become aware that the first industrial revolution is known for the mechanization of production, while those of the second and third for the use of electricity and information technology (IT) respectively.

The fourth industrial revolution embarks upon the digital transformation whose important component is the industrial Internet of Things (IIoT).

The industrial Internet of Things (IIoT) is recognized as the fourth industrial revolution (industry 4.0) as it has caused radical changes in what data can help us grow on an industrial scale. It multiplies productivity, enhances dependability, and fosters competitive performance.

In simple words, IIoT refers to the subset of IoT in which the intelligence of things and devices are used to solve industrial problems related to supply chain management, data privacy risks, cloud strategy, cost containment, and others.

A scholar remarked thus,

IIoT is a persuasive paradigm that connects various sensors around us to the Internet, thus unlocking the window that opens into a fantastic way of life.

Again, IIoT can be understood as an advanced form (or modality) of IoT that integrates networked devices and technologies with the mechanization applications exclusively dedicated to industrial communication.

In the past, many industries relied on wireless technologies that gave ad hoc solutions, but with the emergence of IIoT, cost-effective technologies such as wireless art and ISA100.11a are being developed with increased adaptability.

As stated above, the IIoT is recognized as the 4th industrial revolution or Industry 4.0 due to its timeless contributions to advancing IoT, the Internet of services, automation systems, cyber-physical systems (CPS), cloud computing, and others (figure below).

Industry 4.0 is, therefore, also known as the factory of the future or the smart factory. It creates and fortifies a business eco-system that connects assets, resources, infrastructure, and miscellaneous commercial strategies across the businesses to interact with each other in a decentralized manner to benefit national economies.

To score such a landmark chain of successes, IIoT being one major component of the industry 4.0 has outperformed.

Thus, the difference between Industry 4.0 and IIoT can be better understood in terms of the all-encompassing macroscopic vision of Industry 4.0 in resolving all the industrial networking difficulties whose one major component is IIoT that helps it reach its destination.

industry 4

Why IIoT?

Today, the IIoT has gained a reputable status in the global marketplace due to its extraordinary merits, which are described below.

  • It helps industries run more efficiently as it can combine both information technology (IT) and operational technology (OT) domains.
  • It makes industries reduce costs in capital expenditure (CAPEX) and operating expenses (OPEX).
  • It ensures greater machine-to-machine communication.
  • It helps constitute smart factories that connect all devices embedded with sensors over the Internet to have advanced visibility of ongoing industrial activities via real-time data access.
  • It gives improved customer experience, data confidentiality, constant data streaming, enhanced GDP, and so on.
  • In industrial settings, IIoT is key to monitoring environmental factors and the upkeep of a healthy work environment with the constant detection of smoke and other pollutants in the air.

Some of the IIoT applications are underlined in the figure below.

iiot applications

Industrial IoT Technologies and Infrastructures

Some several IIoT-enabled technologies and infrastructures help gather, store, and process data. Among many, the few are outlined below.

  • Cyber physical system (CPS)
  • Blockchain technology
  • Cloud computing
  • Edge computing
  • Fog computing
  • Big data analytics

Cyber Physical System (CPS)

cyber physical system

It is one of the key technologies that the industry 4.0 or IIoT uses. It incorporates embedded intelligent systems installed in the production machinery, such as a turret lathe, drill press, injection molding machine, or sheet extruder, to connect the manufacturing world with the virtual one (figure below).

The CPS creates smart factories by enabling industrial environments with the integration of networking, computation, and storage capabilities. That’s how smart products are becoming increasingly identifiable as tractable.


Precisely speaking, the CPS consists of any automated distributed systems that connect communication networks and computational infrastructures with the physical environment, such as production machinery.

Contrary to the conventional embedded systems, they immensely rely on industry 4.0 device networking.

As a consequence, they therefore comprise a control unit to manage sensors and actuators that maintain interaction with the physical environment. These automated distributed systems now process the collected data perceived by the sensors and exchange it with either applications or services through an effective communication channel.

During such actions, these systems must be stable, dependable, and secure quite enough. Industry 4.0 therefore offers security support across all CPS network tiers, thus providing security as well as data anonymity at the same time.

Blockchain Technology

To share data with all the parties participating in the IIoT network is not a secure choice. In this vein, blockchain technology offers excellent support for IIoT networks on account of its unique features, for instance, distributed attributes, trackability,  durability, reliability, temper resistance, and built-in data origin, among many others.

So, what is blockchain actually?

It is a particular kind of data structure that is made to implement a distributed ledger with Bitcoin and use public key cryptography in order to ascertain secure peer-to-peer network transactions.

Each link in the chain secures reference to the previous link’s hash. The figure shown below illustrates the workflow of a typical blockchain.

blockchain technology

Several advantages result from this technology. It helps improve IIoT security and ensures decentralized access to the IIoT data with increased transparency.

However, any blockchain transaction would require the approval of all shared nodes in the network, which would subsequently result in more transactions than with a centralized database system.

Its obvious result would be higher latency. It results in the limited throughput of the blockchain due to its complex consensus processes and cryptographic protocols.

Cloud Computing

cloud computing

Huge amounts of data generated by the IIoT need high-speed computer systems that are mainly dispersed and are fundamentally required for processing, analyzing, and storing such data. Technologies used in cloud computing offer a number of services, from computation and networking to storage across all the components of the IIoT.

All the related hardware and software are directly interfaced with the backend clouds. With the emergence of cloud computing and graphic processing units together, the computational power has become immensely rich with much-improved visualization.

In simple words, cloud computing refers to the practice of using a network of remote servers hosted on the Internet to store, process, analyze, and exchange large volumes of IIoT data compared with the local server and personal computer.

Today, the paradigm of cloud computing as a conventional concept for handling huge amounts of data within the IIoT domain has radically transformed upon its integration with AI.

As a consequence of this, modern technologies such as 5G, VR,  AR, and mixed reality are being used not only in industrial applications but in education and medical fields also.

Edge Computing

edge computing

In conventional cloud computing, the data resources and the cloud computing hubs are maintained distance apart. But, edge computing has resolved such a technological impasse to a great extent, if not wholly, then partly.

Edge computing is a computing paradigm used in IIoT that refers to the use of edge devices that have great computational powers and capabilities to execute the local processing of data resources and, thereupon make quicker decisions. The computational results thus received may now be uploaded to the cloud centers.

classification of edge computing

It has proven merits and potential to mitigate problems associated with cloud computing. It considerably lowers the system’s latency and the need for bandwidth. However, the smart industry using edge computational technology needs to arrange effective security measures nearby to avoid data breaches.

Fog Computing

It is also known as fog edge computing. It clubs the best features of both edge and cloud computing: It brings cloud computing nearer or to the edge of the network where the data is generated and utilized.

Fog computing is a computing paradigm used in IIoT settings that refers to the wireless or decentralized gadgets that interact with the network to execute needed operations without the mediation of the external parties. Instead of uploading all the generated data to the distant cloud computing centers, in fog computing, some of the data is (pre-)processed using the fog nodes (fog devices) at the network’s edge where the data is perceived.

In fog computing, fog nodes denote the devices that provide storage, processing power, and virtual network connectivity. It makes use of web applications integrated with the IT infrastructure (figure below).

fog computing

It reduces latency and expedites data processing and, therefore, decision-making. One of the merits is its decentralized computing approach instead of the centralized one used in cloud computing. Now, the security is enhanced due to the data’s proximity to its resources. Scalability is much easier due to the addition of more fog nodes or edge devices.

Big Data Analytics

big data

The IIoT generates huge amounts of data that now need to be stored and processed to draw productive conclusions thereupon.

Big data analytics in the Industrial IoT settings refers to the analytics platform that processes the incoming IIoT data to identify not only useful patterns but also anomalies in the data as well as key trends and related needful insights. At best, it leverages machine learning (ML), deep learning (DL), artificial intelligence (AI), and statistical analyses in order to develop (machine maintenance) predictive models, anomaly detection algorithms, and optimization strategies, among many others.

Thus compared with conventional big data analytics, IIoT-based big data analytics is made to possess unique features in storing, processing, analyzing, and assembling big data analytics operations using ML, DL, and AI tools and applications.

Frequently Asked Questions

When was the concept of IoT first introduced?

IoT was first introduced by a member of the Radiofrequency Identification (RFID) Business Association in 1999 soon after which it kept gaining popularity due to the rise in electronic devices, reliable cloud computing services, and data analytics.

In what sectors has IoT contributed?

IoT has outperformed in sectors such as healthcare transportation, smart homes, and the environment. Today, it is having a great impact on industrially mediated problems by enabling more structured observations at lower costs.

Which is the better IIoT setting: edge, fog, or cloud computing?

It depends upon the particular industrial need. If extremely low latency is required, then edge computing is the best. In case, long-term storage and scalability are required, cloud computing would prove excellent. Whereas, fog computing would be preferable where comparatively low latency is required yet with centralized data analysis and control.

What are the benefits of IIoT-based big data analytics?

Using data collected from the IIoT sensors installed on mechanical machinery, for instance, big data analytics makes industrial operations more efficient, fast, agile, and data-driven. It enables industries to utilize the data generated by their own machinery to make informed decisions in the future in order to reduce operational costs, improve product or service quality, and gain a competitive edge in the market.