IIoT Vs. IoT
Chapter 1 of IoT Infrastructure
IIoT vs. IoT
IoT stands for Internet of Things. IIoT, on the other hand, stands for Industrial Internet of Things. Most people who read the full forms of these acronyms will react in one of two ways:
- See IIoT as just a subset of IoT
- Get curious about the significance of Industrial IoT (after all, you don’t come across acronyms for Agri-IoT, Home-IoT, etc.)
That second reaction gets it right. Why? While technically a subset of IoT, IIoT is a broad and nuanced topic on its own. IIoT and IoT also have core differences and there are several things to consider in an IIoT vs an IoT system.
By the end of this article, you should have a solid understanding of what an IIoT system is, how it differs from a standard IoT system, what factors matter most when evaluating an IIoT system, and typical applications for IIoT systems.
IoT vs. IIoT: An overview
Here’s a summary of the key differences between IoT vs.IIoT. The sections that follow will expand on these comparisons.
|Use cases and applications||Linked to production objectives||Linked to quality of life of the end-users|
|Effectiveness evaluation||Production metrics like OEE, downtime, productivity, etc. measure the effectiveness of an IIoT system||Lifestyle parameters like user experience, value-for-money, ease of onboarding determine the effectiveness of an IoT system|
|Precision and Reliability requirements||Very high, in harsher environments, and over longer lifespans||Nominal, with some exceptions|
|Scale and Volume of data||Orders of magnitude higher than IoT systems||Typically low, with some exceptions|
|Security||Indispensable; compromise here can cause losses worth millions||Extremely Important|
|Maturity||Higher adoption, better pace||Underpenetrated, especially in developing markets|
Similarities between IoT and IIoT
The difference between IIoT and IoT is similar to the difference between a specific brand of automobiles, like Rolls-Royce, and automobiles in general. Both cases have a subset (IIoT and Rolls-Royce) and a superset (IoT and automobiles).
If you were asked to list the differences between Rolls-Royce and the superset of automobiles, you’d list the features that make Rolls-Royce stand out. That doesn’t take away the fact that Rolls-Royce shares many similarities with a generic automobile.
Similarly, IoT and IIoT, share many similarities. They both:
- Use the same basic architectural elements (things, cloud, user interface, etc.)
- Use similar workflows (sense, process, transmit)
- Use many of the same communication technologies (WiFi, BLE, Cellular Networks, LoRaWAN, etc.)
- Use many of the same communication protocols (TCP, UDP, HTTP, MQTT, etc.)
- Can use similar hardware components
With that in mind, let’s move on and look at the differences in IIoT vs. IoT.
|Platform||Real-Time Event Processing||Internet Scale Throughput||Stateful Edge Device Processing||Cross-Region Replication||Geo-Fencing and Data-Pinning|
|Azure IoT Edge||✔️||✔️|
|AWS IoT Greengrass||✔️||✔️||✔️|
IIoT vs. IoT: Use Cases and Applications
Perhaps the most fundamental difference between IIoT and other forms of IoT is use cases. IIoT use cases generally involve industrial applications like those found in manufacturing. Examples of IIoT use cases include:
- Monitoring of shop-floor machines to determine their productivity and uptime,
- Monitoring of electricity consumption in a factory and drawing insights to optimize for the bill
- Automating processes to improve production
- Coordinating schedules and workflows across multiple factories
- Reducing machine downtime through predictive maintenance
IoT, on the other hand, has use cases linked to the improvement of the quality of life for end-users. Examples include:
- Making everyday appliances smart
- Real-time monitoring of vital health parameters through wearables
- Live tracking of vehicle location, drive score, fuel consumption
- Personal voice assistants
- Disaster monitoring and mitigation
IIoT vs. IoT: Effectiveness Evaluation
For instance, if a user purchases a smart air conditioner, then they will check for convenience factors (like voice control and app control) and cost-saving factors (like the presence of smart energy savings mode). A smart AC that requires a lengthy setup for Alexa will be less preferred than one requiring a single-click setup.
For IIoT, on the other hand, effectiveness is linked to the business objectives. It can be cost reduction (operating or maintenance), downtime reduction, efficiency improvement, or maybe all of the above. OEE (Overall Equipment Effectiveness) is a term you’ll hear very often in this regard. IIoT may also be evaluated in terms of ease of equipment monitoring and operator productivity monitoring.
A bad design or UI may severely affect the sale of an IoT product but won’t play as big a role in determining IIoT sales, as long as the IIoT system works as intended. Similarly, IIoT users may accept a complex onboarding process if the system can deliver a business benefit. However, overly complex onboarding is often detrimental to the sale of consumer-grade IoT products.
Store, serve, and process data anywhere in the world
- Improve write performance with globally distributed active-active architecture
- Scale with a real-time data layer, accessible within 10ms proximity of 80% of the global population.
- Support multiple data types (KV, Docs, Graphs and Search) and streaming data
IIoT vs. IoT: Uptime Requirements
An IIoT system must adhere to stringent uptime requirements, especially in settings where the production happens round the clock. Even an hour-long downtime can often result in losses worth thousands, or maybe millions of dollars. This is especially true for use cases where the IIoT system is used for passive monitoring of production and active control across the lines or units.
For this reason, the cloud resources of an IIoT system need to have availability across multiple regions (to provide backup in case of a disaster in one location) and reasonable capacity margins (you don’t want an overloaded server causing a downtime). Therefore, uptime requirements can make an IIoT system more expensive than an IoT system.
On the other hand, minor inconvenience is often the only effect of downtime for an IoT system. While you may strive to ensure that the downtimes are minimum, you will generally not take measures that lead to significantly higher product costs (discouraging price-sensitive consumers).
Edge computing needs a next generation database technology
- Stateful geo-replicated stream processing keeps globally distributed data consistent
- One integrated platform for streams, key values, docs, graphs, and search simplifies development
IIoT vs. IoT: Precision and Reliability Requirements
An IIoT system generally demands higher precision and reliability than a consumer IoT system. The precision generally needs to come from the sensors (leading to higher cost), and the reliability needs to come from all system components.
Consider a connected factory setup where the IIoT device provides a real-time update of the production volume from one step (say packaging) to another step (say labeling). Now, suppose there is even a slight error in this measurement (precision), or the measurement becomes unavailable for some time, or there is a lag in data transmission (reliability). In that case, it can lead to accumulation at the labeling step and choke the system.
Additionally, Industrial IoT devices typically operate in harsher environments (temperature, pressure, humidity) and are expected to last for years or decades. IIoT systems need to maintain their precision and reliability throughout their lifetime.
While consumer IoT doesn’t generally have as stringent precision and reliability requirements, there are exceptions in other IoT categories. IoT for healthcare, for example, can also be subject to similar precision and reliability requirements as an IIoT system.
IIoT vs. IoT: Scale and Volume of data
While you will find exceptions depending on the application, in general, the volume of data transfer in IIoT is much larger (by orders of magnitude) when compared to IoT. One reason for this is the high frequency of data packets. An IIoT device in a real-time application will typically transmit a data packet every couple of seconds. In contrast, an IoT device will generally perform data transfer only a person or system interacts with it.
Data processing is also much more complex and resource-intensive in IIoT applications. This is because IIoT applications typically require many devices to work in coordination. The number of devices working together can scale up to tens of thousands for a large factory. Typical processing involves scheduling, workflow management, interrupt handling, etc.
On the other hand, consumer-grade IoT applications generally involve standalone devices or coordination among a small number of devices. A smart home light can operate standalone or integrate with other smart devices in your home. It will send data packets on every command (like ‘Turn On’) and maybe exchange a couple of health packets per day.
IIoT vs. IoT: Security
While security is important for all IoT applications, it is indispensable for IIoT. The primary reason is the cost impact of a compromised system. An IIoT system disrupted by a cyberattack can cause losses to the tune of millions of dollars per hour. Any accident caused by a compromised system can lead to heavy capital losses that can take time to recover and threaten human life.
Even if the IIoT system is in a passive monitoring role, a cyber attack can leak out vital Intellectual Property (IP) assets and lead to millions of dollars of losses. Therefore, security must be a top priority in an IIoT system.
IIoT vs. IoT: Maturity
While IoT is in the early stages of adoption in several parts of the world, IIoT seems to be better placed and growing more rapidly. The reasons affecting the adoption are different for both.
In the case of IoT, the high cost of a smart device compared to its non-smart equivalent is one deterring factor. Another is a lack of marketing. Smart products are often perceived as ‘premium’ and not meant for the masses.
Voice-controlled devices like Echo Dot and Google Home are accelerating the adoption of consumer-grade IoT systems. However, the market is still underpenetrated, especially in developing nations. For example, while 32% of U.S. homes had smart home products in 2020, only 3% of Indian homes did.
In the case of IIoT, the incentives to adopt are more business-oreinted (higher profits, fewer downtimes, higher productivity, etc.). Therefore, enterprises are adopting IIoT based on business requirements.
However, converting an existing manufacturing facility to an IIoT-enabled facility is not simple. Established manufacturing processes may need to be modified; operators need training, manufacturing may need to stop for IIoT device installation, and, above all, management needs to commit to the initial capex investment.
That said, the adoption rate is signaling a robust trend. A 2015 Accenture study involving 1400 businesses showed that just 7% had an IIoT strategy in place, with equivalent investments planned. However, the adoption is picking up pace. It seems to have been accelerated by Covid-19, as highlighted in a recent independent survey, where 77% of the respondents had deployed at least one IoT project. Even in developing economies, the adoption seems to be healthy. In India, for example, out of 1200 listed large enterprises, 35% have adopted IoT as of 2021.
In this article, we saw what IIoT is and how it compares to IoT. We saw how it is fundamentally and structurally similar to IoT (and therefore a subset) but has different applications, requirements, and challenges than consumer-grade IoT. IIoT is inherently focused on industrial use cases, and business requirements drive adoption. IIoT users have different requirements and expectations than users of consumer-grade IoT products. As a result, IIoT systems are designed to meet the demands of these industrial applications.