IIoT sensors: making the physical world digital

October 18th, 2017, Published in Articles: EE Publishers, Articles: EngineerIT

For all the attention it has garnered in recent years, the industrial internet of things (IIoT) remains in its infancy. Contrary to popular belief, the IIoT is not only about connecting and internet-enabling assets to generate insightful data, but refers to the complete reconfiguration of the supply chain and manufacturing processes around this data.

By merging operational technology (OT) and information technology (IT), the IIoT turns manufacturing into a digital business. Digital businesses have advantages such as scalability, shorter product design times and flexible production. But IIoT also comes with new complexities, in that it shifts manufacturing to an outcome-based economy – one centred on the consumers’ outcomes rather than individual products and services that leave the factory.

Sensors sit at the heart of IIoT and make the physical world digital by forming the nervous system that senses and creates the data that IIoT relies on.

IIoT adoption is premised on the notion that quality data can be analysed to allow real-time control of industrial processes and enable digital simulations to improve operational efficiency (uptime and asset utilisation); and in doing so it will improve profitability by improving reliability, predictability and safety. While many deployments initially focus on asset performance and operational improvements, the long-term goal is automatic decision-making.

Since IIoT is all about the data, it also relates to big data, machine learning and data visualisation technologies such as augmented or virtual reality.

Ecosystems and the IIoT evolution

IIoT’s wide span across multiple domains (digital/data links all the different spheres of the business) makes the full ownership of the digital value chain nearly impossible for a single company. Instead it requires a collaborative approach to create an ecosystem or platforms in which each company can focus on its core competencies. Platforms and ecosystems are also important for enabling data aggregation across industries.

The IIoT ecosystem comprises data generating equipment (sensors, actuators, gateways) which sit atop platforms that integrate and feed the data to applications (e.g. human machine interfaces) through dashboards or other reports, where decisions are made and controlled. The communication of this data too is being standardised, mostly over Ethernet, wirelessly or through cellular gateways.

In its 2015 report on IIoT, the World Economic Forum expects the technology to evolve through four main phases in the foreseeable future: the current, initial phase focuses on operation efficiency, while the second phase looks to develop new products and services made possible by the IIoT (i.e. data monetisation). In the longer term, structural changes include a shift to an outcome-based economy (platform-enabled marketplace) and finally a pull-economy of continuous demand sensing that relies on end-to-end automation and flexible production.

Connected manufacturing takes many forms.

Deploying IIOT

The expansive nature of IIoT also means a deployment approach should be incremental and iterative, yet it still requires explicit design to ensure all the components work together. For this to happen, interoperability on all levels is essential.

Fortunately standards already exist and are still being developed around libraries of interoperable components and semantics-based methods to incorporate components. Manufacturing technical standards such as MTConnect, which retrieve process information from numerically controlled machine tools, and machine-to-machine communication protocols such OPC-UA, are open, non-proprietary specifications that enable interoperability and compatibility needed to connect multiple different devices.

Semantic definitions define the common language to describe elements of manufacturing and are important when it comes to connecting sensors as it makes sensors interoperable, findable and usable by different (software) applications.

The idea of deploying the IIoT is to design everything (including sensors) around the software platforms in the ecosystem.

Sensors and data types

Fig. 1: Zelio NFC timing relay.

Sensors and the data they create make the physical world digital. Sensors’ data streams dictate the type of insight manufacturers are able to derive to enhance and create alternative data streams which could lead to new business models.

It is common practice to start with a monitoring approach when it comes to sensor implementation, before adding more complex functions such as control to the sensors. This is usually the case where legacy systems have to be taken into account. Monitoring typically includes detailed machine usage data, which can be used for predictive maintenance and asset optimisation (uptime). Monitoring data can also provide insight into equipment users.

As more sensors and data streams come online, they provide insight into the relationships between machines in a system or process. Furthermore, multiple data streams can be combined, and along with external data can add even more detailed analytics and insight into production processes. Adding control functions to sensors, such as having them automatically optimise processes in machines like drives for example, is where the real potential of IIoT comes into being.

Legacy systems which don’t have monitoring and control built in need not to be excluded or immediately made redundant. New and cheaper types of sensors (e.g. Raspberry Pi computers) are able to be fitted to legacy systems – usually in monitoring applications – to bring the systems into the digital realm.

When deciding which sensors to deploy, Modern Machine Shop’s Mark Albert recommends asking three main questions:

  • What changes deserve attention or require a reaction?
  • What decisions do sensor data influence?
  • What value can be derived from these decisions?

Examples of IIoT sensors

Fig. 2: Motion servo drive.

A near-field communication (NFC) time delay relay, like the Schneider Electric Zelio NFC timing relay (Fig.1) is a multi-function timing relay that is enabled and controlled by NFC technology in smartphones through an Android app. It enables the user to retrieve and configure product settings and diagnose product status. It also makes monitoring and testing easier.

Schneider Electric’s Lexium 28 IO servo drives (Fig. 2) come standard with digital and analogue I/O, an interface for CANopen/CANmotion fieldbus and an encoder interface for servo motors. The servo drives incorporate functions such as auto-tuning, position, speed, torque control, and the position sequence mode. They offer automatic motor identification, status monitoring and log function, making them suitable for a range of applications.

There are also sensors like the RS Pro LCD digital power meter, a multifunction modbus power meter which covers a range of parameters and applications. This model works as a CT operated meter, allowing for a retrofit installation. It can measure parameters including active and reactive energy, power and current demand, phase-to-neutral voltage and harmonic distortion, current, power factor, and frequency.

Considerations, risks and concerns

Fig. 3: Digital power meter.

The reliability of systems is an important consideration, especially as the performance requirements of manufacturing are very high. A server glitch could have profoundly adverse effects on a factory floor, not to mention for a utility.

Cybersecurity and data privacy remain challenges for IIoT, as does the lack of interoperability among systems. All these pose a risk of significantly increasing the cost and complexity of deployments. Data management itself can be a stumbling block.

The high cost of technologies (especially sensors) as well as the lifespan of products are also worth keeping in mind. A World Economic Forum and Accenture report on IIoT lists further risks as including uncertain returns on investments on new technologies, immature or untested technologies, the lack of data governance rules across geographies, and a shortage of digital skills.

Learn more

There are consortia working to address industry collaboration on IIoT, including the Industrial Internet Consortium (IIC), the AllSeen Alliance, and the Open Interconnect Consortium (OIC). AllSeen and OIC focus on device-level connectivity, while the IIC aims to accelerate the adoption and deployment of industrial internet applications.


[1] Mark Albert, “7 Things to Know about the Internet of Things and Industry 4.0”, Modern Machine Shop, 09-Jan-2015. [Online]. Available: https://www.mmsonline.com/articles/7-things-to-know-about-the-internet-of-things-and-industry-40. [Accessed: 09-Oct-2017].
[2] “Industrial IoT & Industry 4.0”, RS Components. [Online]. Available: http://uk.rs-online.com/web/generalDisplay.html?id=industrial-iot. [Accessed: 09-Oct-2017].
[3] “IoT Platforms: The Engines for Agile Innovation”, Accenture. [Online]. Available: https://www.accenture.com/us-en/insight-iot-platforms-agile-innovation-scale. [Accessed: 09-Oct-2017].
[4] Harald Bauer, Mark Patel, and Jan Veira, “The Internet of Things: Sizing up the opportunity”, McKinsey & Company, Dec-2014. [Online]. Available: http://www.mckinsey.com/industries/semiconductors/our-insights/the-internet-of-things-sizing-up-the-opportunity. [Accessed: 09-Oct-2017].
[5] “Industrial Internet of Things: Unleashing the Potential of Connected Products and Services”. World Economic Forum, Jan-2015.
[6] B. I. Intelligence, “Will the Internet of Things be bigger than the Industrial Revolution?”, Business Insider Deutschland, 18-Aug-2016. [Online]. Available: http://www.businessinsider.de/iot-ecosystem-internet-of-things-predictions-and-business-potential-2016-7. [Accessed: 09-Oct-2017].

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