Whether designing, deploying or operating industrial IoT systems, knowing how it differs from traditional IoT is a must.
The concept of the Internet of Things is primordial to the industrial IoT. However, how devices work in a smart home or office is vastly different from how they work in an industrial operation, such as in, for example, an intelligent car assembly factory floor.
The number of connected IoT devices worldwide by 2021 was 11.3 billion. By 2030, Statista estimates there will be 29.4 billion IoT devices. This means that there will be more than three devices for every person in the world today.
Proportionally, consumers are not the most significant users of IoT devices. Major industries in energy, water, manufacturing, government, transportation and the natural resources sector use billions of devices combined.
IIoT is defined by SAP as an AI-powered “system of systems” that can curate, manage and analyze data from end to end in industrial operations. The system includes machines, sensors, and other connected devices and systems that work together in real-time.
When the data generated by the connected components of an IIoT infrastructure can be leveraged using machine learning and AI applications, industries can drive efficiencies, learn from mistakes, increase productivity, visibility and much more.
IIoT networks use machine-to-machine communication to “talk” among devices. These devices also regularly transmit and receive data to and from a central system that integrates and manages all IIoT devices. The central system can operate in the cloud, on the edge or in data center locations. IIoT devices are usually connected via 5G, Bluetooth Low Energy BLE, Wi-Fi and near-field communication NFC.
IIoT benefits include more efficient machines, smarter management and increased security for workers. Automating industrial operations protects workers from performing dangerous tasks and helps companies reduce human labor costs and increase speed and agility.
IoT is a term used to describe the network of physical objects embedded with sensors, software and other technologies. The primary purpose of this network is to connect and exchange data with other devices and systems used online. IoT devices are varied: They may be household devices or sophisticated industrial tools.
IoT shares common ground with IIoT. Systems and devices are connected and communicate, and users use a centralized platform to control them. Additionally, IoT uses edge and cloud computing alongside analytical features. The main difference between them is defined by their end use. In IoT, the end users are consumers, offices and other work environments like healthcare.
The idea behind IoT is to automate many tasks that used to be manually completed and integrate systems for better accessibility. For example, in homes, users can manage all their smart devices using a central hub or smartphone, usually equipped with voice control. IoT environments are designed to make life and work easier, smarter and more accessible.
IIoT can be thought of as an IoT with super-enhanced capabilities. It’s fundamental to understand the differences between one and the other, primarily if you work in industries or environments that require intense machine collaboration, cooperation and connectivity.
The main difference, as mentioned above, is the end user. The end user, in both cases, defines what the devices and network are capable of and what their features are. IoT is designed and operates in homes, offices, buildings and professional work areas. While health IoT can be highly advanced, they are still closely related to consumer devices, not industrial ones. In contrast, the end user of IIoT is bigger in scale. Work in the industrial sector requires different devices and connected systems and networks.
Another main difference is how both groups use machine learning and AI. Home and office IoT devices will use analytics and AI-powered applications. However, they do not leverage data as extensively as IIoT does.
For example, factories using IIoT can run AI algorithms that analyze the data each device generates and can adjust individual operations for each device to increase production. Therefore, IIoT systems can “learn” and become more efficient. This advanced analytics does not occur in consumer-end IoT systems. IIoT AI systems can also automate operations from security to redundancy or maintenance.
While not all IIoT devices and systems are significant in size, they are all built to endure extreme conditions. High and low temperatures, exposure to weather, water, dust, friction and prolonged lifecycles are necessary for industrial sectors. Compared to IoT devices and networks, IIoT is durable and resilient. They are also designed to be repaired and maintained. Additionally, IIoT performance is intense, so both software and hardware have to be built accordingly.
IIoT systems are designed for mission-critical processes, and durability is essential. Industries can not afford downtime or disruption in their systems. Backup systems are usually implemented as contingency plans in case one component of the IIoT infrastructure fails or requires maintenance.
Industries using robotics, sensors and systems demand levels of precision that exceed the standards of domestic IoT systems. IIoT also needs to be scalable. While work or home environments may connect a couple of dozen devices, industries can have hundreds or thousands of devices connected to a network, and they need to be able to scale their IIoT system if demand increases.
Additionally, the amount of data generated in IIoT infrastructures is exponentially higher than that generated in other IoT areas. The challenge of transferring this data in real-time while keeping the information secure is unique to IIoT. Similarly, industries usually use private networks to manage their data flows, with private 5G networks becoming the new norm.
All IIoT big data must be integrated and analyzed to be leveraged to optimize operations. The central software and platforms used in IIoT are designed explicitly by top vendors for industrial purposes. They can manage big data from devices, workers, communications and external factors such as supply chains, partners or market alterations. Once these systems compute and analyze the factors, they use AI to automatically adjust operations with zero human intervention.
The final difference between IIoT and IoT is its complexity which creates several challenges in security. The complexity also increases the required skills needed to design, deploy and operate these systems. Talent in IIoT is a critical sector in demand. Skilled professionals are required for all elements of IIoT. This is a significant difference, as domestic IoT does not usually require users to have any technical skills. Workers in industries need to be trained and skilled. While new technologies like augmented reality are streamlining these processes, these positions are still considered highly technical.
Security is another differentiator. In IIoT, security consequences can affect the company, its suppliers, partners, all its clients and even the population. For example, security for energy or water IIoT systems can be a matter of national security. Companies using IIoT have to secure the networks, the software, systems and platforms, and all other devices, making sure they cannot be digitally or physically manipulated. IIoT’s solutions include secure and resilient system architectures, specialized chipsets and hardware, encryption and authentication, threat detection and risk management frameworks.
The world is deeply immersed in a transformation and modernization journey. In this era, IoT is bringing together the digital and real world. Connected devices, machines, robotics, systems and data centers are the vanguard of development. Understanding the main differences between IoT and IIoT is the first step in designing, deploying or building innovative architectures that can drive outcomes and efficiencies in your industry.
If you’re working toward implementing IIoT within your enterprise, selecting the right software is critical. There are hundreds of IIoT platforms and each one is slightly different from the next, so how do you choose? This article, including links to TechRepublic Premium resources, can help.