The Industrial Internet of Things (IIoT) is defined as a set of devices and applications that allow large enterprises to create an end-to-end connected environment from the core to the edge. It also includes traditional physical infrastructure, such as containers and logistics trucks, to collect data, react to events and make more informed decisions with the help of smart devices.
The Industrial Internet of Things (IIoT) is an extension of the Internet of Things (IoT) with many applications in the consumer sector. IoT use cases include, for example, smart home devices such as the Amazon Echo, which use Alexa voice recognition to turn off lights remotely.
In industrial operations, this technology has been applied commercially on a large scale in environments with complex infrastructure and large equipment. By contrast, the industrial Internet of Things can remotely manage heating, ventilation, and air conditioning (HVAC) systems across an entire factory. This is just an industrial Internet of Things use case to simplify and improve the management of enterprise operations.
The Industrial Internet of Things is a subcategory of the Internet of Things, where companies are redefining how to connect, monitor, analyze and act on industrial data to reduce costs and boost growth.
The idea behind the Industrial Internet of Things is to use data generated over the years by "dumb devices" in industrial facilities. Smart machines on the assembly line not only capture and analyze data faster, but also communicate important information faster, which helps make business decisions faster and more accurately.
The integration of information technology (IT) and operational technology (OT) is driving the development of the industrial Internet of Things. It is a network matrix that connects devices and devices, collects data through sensor technology, analyzes data, and integrates it directly into the platform as a service. The Industrial Internet of Things will herald a new era of industrial use cases, with many opportunities for economic expansion.
The Industrial Internet of Things collects large amounts of field data from the factory floor, transmits it through connecting nodes, analyzes it on servers, and transforms the information into actionable insights on cloud platforms. This encourages businesses to make better decisions for their specific markets and target audiences.In other words, the Industrial Internet of Things is a system that connects edge devices such as actuators, sensors, controllers, connection switches, gateways and industrial personal computers (IPC) to the cloud.
Industry 4.0 is the product of the fourth industrial revolution. The fourth industrial revolution is defined by the integration of traditional automated manufacturing with industrial processes driven by smart technologies and autonomous communication devices.
The term "Industry 4.0", abbreviated as I4.0 or I4, appeared in 2011 as an initiative of the German government to vigorously promote the digitalization of industrial processes over the past 20 years.
As outlined by BCG, the Industrial Internet of Things is the main pillar of Industry 4.0, alongside additive manufacturing or 3D printing, augmented reality (AR), autonomous robotics, big data analytics, cloud computing, cyber security, horizontal and vertical systems integration, and simulation.This is due to the ability of autonomous communication between machines and a decentralized digital environment to automatically solve problems that previously required human intervention.
Industry 4.0 covers industrial Internet of Things, digitalization and sustainable development of enterprises in a broader scope. The Industrial Internet of Things is the driving force behind Industry 4.0, without which there would be no Industry 4.0. In other words, the industrial Internet of Things is limited to data detection, data transmission, data computing, data processing and intelligent applications in specific fields.
A typical industrial IoT architecture describes an arrangement of digital systems that collectively provide networking and data connectivity between sensors, IoT devices, data storage, and other layers. Therefore, the industrial Internet of Things architecture must have the following points:
These are groupings of networked objects that sit at the edge of the IoT ecosystem. These locations are as close to the location of the data source as possible. These are typically wireless actuators and sensors in industrial environments. A collection of a processing unit or small compute device and a set of observation endpoints.Edge IoT devices may include traditional devices in brownfield environments, cameras, speakers, sensors, and other meters and monitors.
What happens at the furthest edges of the web? Sensors acquire data from the surrounding environment and the objects they monitor, and then transform the information into indicators and numbers that the Internet of Things platform can analyze and turn into actionable insights. The actuator control that processes that occur in the observe environment. They change the physical environment in which the data is produced.
Without high-quality, massive amounts of data, sophisticated analytics and artificial intelligence cannot reach their full potential. Even at the sensor level, data processing is possible.
In this regard, edge computing provides the fastest answer because the data is preprocessed at the edge of the network, at the sensors themselves. Here, numerical and aggregate data can be analyzed. Once the relevant insights have been gathered, it is possible to move to the next stage rather than sending all the information gathered.This additional processing reduces the amount of data sent to the data center or cloud.
The preprocessing capabilities of edge devices are limited. While as close to the edge as possible to limit the consumption of local computing power, users will need to leverage the cloud for deeper and more thorough processing.
At this point, you must choose whether to prioritize the agility and immediacy of edge devices or the advanced insights of cloud computing. Cloud-based solutions can perform a significant amount of processing. Here, data from different sources can be aggregated and provide insights not available at the edge.
In the context of the industrial IoT architecture, the cloud will have:
Hub: In addition to telemetry and device control, it provides a secure link to the field system. If desired, a hub can provide remote connectivity to a local system across multiple locations. It maintains all communication elements such as connection management, secure communication channels, and device authentication and authorization.
Storage: Used to store information before and after processing.
Analysis: It is helpful for data processing and analysis.
User interface: Provides visualization of the analysis results to the end user, typically via a Web browser interface, but also via email, SMS, and phone alerts.
Here, sensor data is collected and turned into digital channels for further processing at the Internet gateway. After obtaining the aggregated and digitized data, the gateway transmits it over the Internet so that it can be further processed before being uploaded to the cloud. The gateway is still part of the data collection system at the edge.It is adjacent to the actuators and sensors and performs preliminary data processing at the edges.
Gateways can be deployed as hardware or software:
Hardware: Hardware gateways are autonomous devices. Both wire-based (analog and digital) and wireless interface are provided for downstream sensor connectivity. Internet connectivity is also provided, either locally or via a standard link to the router.
Software: On a PC, you can install a software gateway instead of connecting a hardware gateway. The software runs in the background or foreground and provides upstream and downstream communication links as hardware entry points, and the PC provides the physical interface. The software-based gateway can access visual sensor settings and sensor data presentation through a user interface.
Protocols are required to transmit data across industrial IoT systems. Ideally, these protocols should be industry standard, well-defined, and secure. The protocol specification may contain the physical characteristics of the connections and wiring, the procedures for establishing the communication channel, and the format of the data sent over the channel.
Some common protocols used in industrial IoT architectures include:
Advanced Message Queuing Protocol (AMQP): This is a connection-directed, bidirectional, multiplexed, compact, data-encoded message transport protocol. Unlike HTTP, AMQP is built for IIoT-oriented cloud connectivity.
MQ Telemetry Transport (MQTT): This is a compact client-server message transport protocol. MQTT benefits IIoT devices because of its short message frame size and minimal code space.
Restricted Application Protocol (CoAP): This is a datagram-oriented protocol that can be deployed over the transport layer, including User Datagram Protocol (UDP). CoAP is a compressed version of HTTP developed for IIoT requirements.
Industrial IoT systems are now able to coordinate, monitor and control operations across the entire value chain. These platforms control device data and manage analytics, data visualization, and artificial intelligence (AI) tasks for edge devices and, in some cases, transport sensors directly into the cloud and back.
The Industrial Internet Reference Architecture (IIRA) can be used as a reference for developing complex systems in the field of industrial Internet of Things. In general, IIRA's framework advocates that enterprises use a systematic approach to framework design that includes feedback and iteration. In addition, the report recommends customizing industrial IoT designs for specific business sectors, such as energy, healthcare, transportation, and government use.
The biggest advantage of the industrial Internet of Things is that it can help enterprises achieve automation, thus maximizing operational efficiency. In addition, physical devices can be connected to software solutions via sensors to continuously monitor performance. This allows businesses to better understand the operational efficiency of specific equipment and fleets as a whole.In addition, the Industrial Internet of Things enables data-driven decision-making and remote monitoring of all production processes.
Organizations with IoT manufacturing processes may increase their productivity by increasing device usage. As mentioned earlier, network devices provide a continuous stream of data that provides insight into the operation of the device. This improves the overall efficiency of the equipment and maximizes the performance of the machine during runtime.In addition, the use of industrial Internet of Things devices has also increased the utilization of human capital. Smart devices can be used to perform trivial, repetitive, and dangerous activities, freeing employees for other, more strategic, production-related tasks.
The use of the Industrial Internet of Things forces companies to automate production operations. Eliminating human factors from industrial operations can eliminate the inefficiencies that cause defective products to leave the assembly line. With the reduction of quality defects, the profitability of enterprises will increase due to the improvement of customer satisfaction and brand recognition.
Predictive maintenance is a strategy for avoiding asset failures by analyzing production data to discover patterns and predict impending problems.
Integrating industrial IoT sensors into industrial devices enables state-based management notifications. These sensors record the temperature, humidity, and other environmental variables in the work area, as well as the composition of the material and the effects that shipping factors have or may have on shipping. All of this data is useful for predictive maintenance.As a result, asset failures are avoided, expenses are reduced, and machine downtime is minimized.
Smart manufacturing enables greater security, with all industrial IoT sensors working together to monitor employee and workplace safety. Comprehensive safety systems protect workplaces, production lines and personnel. Once an incident has occurred, the entire facility can be notified, activities can be stopped, and senior management can mediate to resolve the problem.This event may also yield useful information that can be used to avoid future recurrence of such events.
Industrial operations are a major source of global electricity supply, which is detrimental to sustainability and the overall bottom line. Constantly monitoring the system with sensors and small devices may uncover inefficiencies that lead to waste. This includes not only monitoring equipment, but also integrated services, such as regulating the temperature, water, humidity and lighting of equipment.In addition, with the progress of Internet of Things technology, sensors consume less energy, which is undoubtedly a boon.
The Industrial Internet of Things can help improve the delivery of field services. It is determined by aspects such as time, context, and technician involvement in a particular service operation. Industrial IoT also allows for real-time data visibility. This means that original equipment manufacturers (OEMs), end consumers and any other interested parties understand the risks and difficulties that arise, resulting in a positive experience.
Today, the Industrial Internet of Things is a major product for large enterprises and one of the key products offered by major cloud providers such as Microsoft and Amazon Web Services (AWS). The Industrial Internet of Things extends the capabilities of advanced data analytics and the cloud to industrial applications such as equipment maintenance, plant operations, supply chain management, and personnel security.Data from the Industrial Internet of Things platform can even help simulate and test products in a digital environment, perfectly integrate digital systems with physical systems, and exponentially improve industrial results.
