IoT vs. IIoT

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IoT vs. IIoT

The purpose of this article is to highlight the significant differences among the Internet of Things (IoT) and Industrial Internet of Things (IIoT), and while walking through the listed considerations, the reader will have the chance to learn about their ecosystems and the particularities of their applications. Moreover, the gaps in the standardization of the technologies related to the IoT are presented along with the current initiatives from various institutions for mitigating these gaps.

Common Ground

Another similarity constitutes the domains for which their application is designed, as well as the standards and regulations they have to conform to in order for them to be successful and commercially available.

The IoT Landscape

The IoT Standards Landscape (ETSI, 2017)

Figure 1:The IoT Standards Landscape (ETSI, 2017)

The figure above summarizes the current situation in the IoT world with respect to the standards and the application domains in which these types of products are used. The landscape is valid for both IoT and IIoT products. Both of them are used in various applications which extend from home automation up to farming and agriculture — vertically. On the horizontal, we can find common telecommunication technologies that are used in all the presented application domains. As we can infer from the figure, the application domains are densely populated by several SDOs (standard developing organizations) and industrial or public alliances.

The image shows two very critical drawbacks for the future IoT standards. Firstly, there are too many players on the field and secondly, the SDOs are mostly independent and there is little reuse between them, and to some extent they are competitors in the race to be the first and most relevant in the market.

The Big Gaps

Based on the IoT landscape image, ETSI (a European Standards Organization — SDO) published an interesting analysis where they identified six main gaps in the IoT:

1. Duplication of IoT architectures and models

2. A large number of communication protocols address heterogeneous types of communication requirements

3. Data models are developed on a proprietary basis and mostly specific to the vertical domains to which they apply

4. Processing rules and decision-making processes under the reception of sensor data lack in harmonization

5. Security and privacy are addressed on an isolated basis for some of the applications

6. Ease of use and maintenance after purchase would require a more global approach

The IoT Technology Stack

The IoT Technology Stack

Figure 2: The IoT Technology Stack

The technology basis for any IoT application — whether intended for the consumer or for industry — is the same, and can be abstracted as shown in the figure. Therefore, building a product from scratch and calling it an IoT product requires expertise in six different domains (Dunkels, 2019):

  1. Device Hardware
  2. Device Software
  3. Communications
  4. Cloud Platform
  5. Cloud Data
  6. Cloud Application

Additionally, the security aspect of each of these components shouldn’t be forgotten. In the interest of a simplified representation, this aspect is not accounted here, as this would require its own dedicated chapter.

Device Hardware

Device Software


Cloud Platform

The Magic Quadrant for Cloud Infrastructure (Gartner, 2020)

Figure 3: The Magic Quadrant for Cloud Infrastructure (Gartner, 2020)

Cloud Data

Cloud Applications

Ten Differences between IoT and IIoT

The IoT and IIoT application domains (Bureau, 2017)

Figure 4: The IoT and IIoT application domains (Bureau, 2017)

While many people assume functionality distinguishes the IoT from the IIoT, the reality is not that simple. Although the IoT and IIoT share common technologies, the similarities end there.

First, to start with the definition of the two. The term IoT, Internet of Things, is often used to refer to ubiquitous, consumer-oriented IoT products.

The IIoT, on the other hand, stands for Industrial Internet of Things, and is a subset of the larger IoT, focusing on the specialised requirements of industrial applications such as manufacturing, oil and gas, and utilities. IIoT is a collective term that describes the connection of machines and production plants.

According to (Ehrenreich, 2018) and (Chan, 2018) the parameters that differentiate the IoT from the industrial IoT include:

· Security

· Interoperability

· Scalability

· Precision

· Programmability

· Low Latency

· Reliability

· Resilience

· Automation

· Serviceability


On the other side, operation safety is not necessarily an issue in an IoT ecosystem as these systems do not handle industrial processes. Therefore, no serious safety incident can happen if a wrong decision is made.


The IoT Interoperability Permutations (, 2017)

Figure 5: The IoT Interoperability Permutations (, 2017)

Usually, industrial IoT applications must co-exist in environments with significant amounts of legacy operations technologies (OT). These legacy OT don’t go away so fast; therefore, industrial IoT solutions must integrate, support various protocols and data sets, and work reliably with these systems.



Example for increasing the positioning accuracy (Fahem Zafari, 2019)

Figure 6: Example for increasing the positioning accuracy (Fahem Zafari, 2019)

Industrial operations require higher levels of accuracy. Automated, high-speed machinery is synchronized to a matter of milliseconds. Therefore, the quality must be assured for such systems. Any small variation in the operation of such high-volume manufacturing processes must be corrected right away. Otherwise, this can result in lost efficiency and downtimes which can affect the revenue considerably.


Low Latency


A wrong action can put people’s lives at risk.

Remember that the “reliability” aspect is part of the SRP triad: Safety, Reliability and Productivity.


The Resilience Model in Four Acts (Delic, 2016)

Figure 7: The Resilience Model in Four Acts (Delic, 2016)

Breakdowns in one part of the system are not unusual in such highly scaled industrial applications; therefore, the applications must be designed with resilience in mind. To compensate for such faults in the system, the IoT system architectures must be designed to always satisfactorily complete their processes and operations.


The Short History of Automation in IT (Lueth, 2015)

Figure 8: The Short History of Automation in IT (Lueth, 2015)

Most of the industrial applications are highly automated from the bottom to the top, with limited or absolutely no human intervention. Such IoT solutions are required to support a range of autonomous actions. In order to ensure this, applications do incorporate intelligence into the edge devices, or deep learning capabilities in the system design.



Anyone considering to develop a new IoT/IIoT ecosystem should evaluate both of these common and distinct characteristics and decide together with the customer on the best suitable architecture without compromising the intention of the application.

One of the key takeaways is that when building industrial IoT products, one has to focus on the real needs and values of a properly designed cloud data-based architecture with a special focus on the safety, reliability and productivity aspects. Clodless IIoT: is this possible?


Bureau, E. T. (2017). Consumer IoT to be Bigger Market than Industrial IoT. Von

Chan, B. (2018). Ten ways IoT differs from IIoT. Von abgerufen

Delic, K. A. (2016). On Resilience of IoT Systems. Von abgerufen

Dunkels, A. (2019). Technical Skills Needed For Professional IoT Projects. Von abgerufen

Ehrenreich, D. (2018). IoT vs IIoT differences you must know. Von abgerufen

ETSI. (2017). IoT Standards Landscape. Von abgerufen

Faheem Zafari, A. G. (2019). A Survey of Indoor Localization Systems and Technologies. Von abgerufen

Gartner. (2020). Magic Quadrant for Cloud Infrastructure and Platform Services. Von abgerufen

Lueth, K. L. (2015). Will the Industrial Internet Disrupt the Smart Factory of the Future. Von abgerufen (2017). Making Sense of IoT Interoperability. Von abgerufen

About The Author

Teodor L. Vasile is an Embedded System Engineer and the IoT Service Lead of ERNI Germany. He is a lecturer for IoT and Embedded Systems at the FOM University ...