The Modern Connected Enterprise: A Foundational Guide to Industrial IOT

Industrial IOT is a subclass of the Internet of Things that aims to improve efficiency by using software, networked sensors, and machines to acquire and analyze data. It is one of the most essential components in the strategic vision for Industry 4.0.

IIOT is the next revolution in industries like mining, energy management, manufacturing, power generation and food processing. IIOT has the capability to improve reliability, efficiency and reduce human intervention.

The combination of sensors, computing units, remote monitoring systems and servers introduces an exponential positive effect on machines that have powered modern innovation for about three centuries. Nevertheless, enterprises have to overcome certain hurdles on their way to full IIOT integration.

Basic Technologies Behind Industrial IoT

There are various technologies in the electrical, mechanical and computing domains that make Industrial IOT possible. From capturing data to drawing insights from dashboards, the hardware and software tools behind these technologies make manufacturing and logistics more efficient. Here are some of the technologies behind IIoT.

Sensors and Actuators (Edge Devices)

Sensors are devices that convert physical quantities to electrical signals. They are capable of detecting changes in parameters like pressure, humidity, light, and temperature. Examples of sensors include Force Sensitive Resistors, thermistors and ultrasonic sensors.

Actuators, on the other hand, convert electrical signals to physical changes in the industrial environment. Hydraulic rams, electric motors and pneumatic cylinders are actuators. They are responsible for actions like opening valves, adjusting the speed of motors and moving robotic arms. Sensors and actuators are the heartbeat of industrial IOT because they allow machines to interface with the physical world.

Edge Computing

Edge computing is a distributed computing model that processes data at the source of creation, as opposed to sending it to a distant centralized data center. This process reduces latency, speeds up data computation and enhances cybersecurity. In edge computing, a sensor, such as a temperature or pressure sensor, collects data.

The data is transferred to a computing unit or server, which analyzes it. The computing unit extracts the most important information and uses it to make decisions. If there is a need, some data might be sent to the cloud for further processing. Critical functions like triggering instant alarms and cleaning sensor data can be done faster with edge computing.

Cloud Computing

Cloud computing serves as storage for the large amount of data acquired from IIoT devices. Industries can use cloud computing services by making use of storage, servers, and software over the internet without owning and managing data centers.

Cloud computing is the key technology behind the scalability and robustness of Industrial IoT. The five most popular cloud computing platforms include:

  • Amazon Web Services (AWS)
  • Microsoft Azure
  • Google Cloud Platform (GCP)
  • Siemens MindSphere
  • GE Predix

Cybersecurity

Cybersecurity is one of the most critical components in the IIoT world because of the existence of malware, computer viruses and ransomware. There is a great need to protect the PID controllers, manufacturing lines, grids, robots, pipelines and other assets.

A cyberattack can cause production downtime, safety hazards and equipment damage. A successful cyberattack can cause the loss of millions of dollars. One important cybersecurity measure in Industrial IoT is to perform regular software and firmware updates.

By addressing patch vulnerabilities, there is a lower chance that hackers will be able to install malicious code. Also, device authentication methods such as identity-based access control and secure keys ensure that only users and trusted devices can communicate.

Big Data Analytics

Big Data Analytics is the means by which industries are able to analyze and draw insights from the large amount of data that they generate. The “big data” comes from things like SCADA/HMI systems, maintenance logs, industrial controllers and inventory systems.

The data gets stored in distributed storage systems, data lakes and time-series databases. Machine learning algorithms are used for computation, while data visualization is used for drawing insights and making decisions. The categories of analytics include:

  • Predictive analytics: This involves trying to find out what could happen in the future, such as a generator that has a 68% chance of failing in the next 1 month.
  • Descriptive analytics: This process aims to identify incidents or mishaps that have occurred. An example is finding out that the temperature of a machine has spiked to 100 degrees Celsius.
  • Prescriptive analytics: Prescriptive analytics helps to know the next course of action before or after an industrial mishap occurs. For instance, engineers can identify that a motor needs to be replaced within 72 hours to prevent a breakdown.
  • Diagnostic analytics: This type of analytics involves finding out the reason why an event occurred, such as an overheating caused by a worn-out bearing.

This technology allows businesses to identify patterns, understand production quotas and consumer behaviour.

Connectivity and Network Protocols

To make a smart factory or industrial system work optimally, several devices have to communicate with each other. All the IIoT devices themselves need to be connected to the internet. The most common method for connecting IIoT devices to the internet is via industrial Ethernet, given the need for low latency and high speed. Other connectivity technologies used in IIoT include:

  • Bluetooth: Used for short-range asset tracking and in applications where low power consumption is needed.
  • Wifi: Used for high-bandwidth applications.
  • LPWAN: Ideal for low-power and long-range monitoring, such as farmland sensors and tank level monitoring.
  • Cellular network: Perfect for wide-area coverage, ultra-low latency and remote sites.
  • Satellite IoT: Great for remote mining sites and offshore oil rigs where cellular networks don’t exist.

IIoT network protocols operate in three layers which include gateway-to-cloud, device-to-device and device-to-gateway. Two industrial communication device protocols are OPC-UA (Open Platform Communications - Unified Architecture) and Modbus (RTU/TCP). OPC-UA supports complex data models and is secure. Modbus RTU and Modbus TCP are used for close-proximity communication and enterprise-level applications, respectively.

Digital Transformation

Long-term Benefits of Digital Transformation for Manufacturers and Industrial Organizations

Industrial IOT is the key to massive productivity in today’s capitalist world, where profit is the driving force. Here are a few advantages of the Industrial IOT revolution.

Data-driven Decisions

With software tools from companies like Siemens and AWS, companies can use machine learning to spot energy waste and underperforming machines. Therefore, they can make the right changes to improve production speed, yield and output.

With the use of GPS trackers, RFID tags and interconnected logistic networks, companies can hasten raw material extraction, reduce shipping delay and improve delivery time. IIOT and industry 4.0 systems also provide dashboards where managers can see production KPIs, machine status and downtime reasons.

They can draw action plans without waiting for weekly reports. IIOT helps organizations to build business intelligence by making data-driven decisions.

Better Predictive Maintenance Measures

Predictive maintenance has to do with servicing a machine at a predicted future date before any failure occurs. This is in sharp contrast to corrective maintenance, where repairs are done after systems break down.

Traditionally, predictive maintenance involved using calendar-based checks and physically visiting sites with handheld tools like stroboscopes and infrared thermometers. However, these processes are often time-consuming and subject to human error.

Industrial IOT can generate a large amount of data on parameters such as temperature, machine speed, production counts and vibration. Instead of manually recording data or using historical estimates, technicians, engineers and managers can know the operational conditions of the machines at every moment.

Remote Monitoring of Assets and Means of Production

With the necessary actuators installed, IIOT systems can remotely do things like:

  • Reset or restart an engine
  • Adjust valve positions.
  • Change the RPM of a machine.
  • Change the pressure or temperature of a system.

With IIOT, an engineer who is not physically present in a factory can shut down a malfunctioning motor or drive. Furthermore, sensors can also monitor abnormal incidents such as pressure spikes, electrical faults, gas leaks and abnormal vibrations. Hence, IIOT systems can send safety alerts to important people like maintenance teams and safety officers.

Risks and Challenges Associated with Industrial IOT (IIOT)

Just like any other technology, IIOT has its downsides. These downsides are associated with cost and malicious actors. Here are some of them.

Device Malfunction and Security Challenges

Risks and Challenges

Sensors can fail and begin to give wrong readings. Due to bugs, software can experience downtime. Electrical and mechanical faults can cause actuators to fail.

Also, disgruntled former employees or hackers can decide to tamper with the industrial system to make it malfunction. Another major challenge is that many IIoT devices are deficient in features like device authentication, data encryption and authorization controls.

Difficulty in Integrating Current Factory Equipment with IOT

In most factories, the production line and other allied equipment are not designed for internet connectivity. They were built decades ago when IoT was not yet a thing. Therefore, most old machines don’t have data ports, Ethernet modules and IoT sensors. They were never designed to communicate with other machines.

Furthermore, the various industrial equipment in a factory tends to have different communication protocols and operating systems. This is because manufacturers use equipment from different suppliers, such as ABB, Schneider Electric, and Mitsubishi, thereby increasing the complexity associated with IoT integration.

High Deployment Cost

The development of a fully fledged IOT system will cost billions of dollars and require the replacement or upgrade of old equipment. The deployment of industrial WiFi, the installation of edge computing servers, or the laying of new Ethernet cables is expensive.

Also, Industrial systems tend to use well-designed devices that withstand the harsh conditions of the industrial environment. An industrial sensor can cost 15 times more than a consumer-grade IoT sensor.

Furthermore, there will be a need to train new workers or retrain existing employees to manage the vast amount of data generated by the IIoT system. The employees will learn big data analytics and new safety procedures.

Key Regulations that affect Industrial Internet of Things (IoT)

Just like any other emerging industry, governments and private agencies have been setting up laws and regulations to guide the operation of smart factories. These standards relate to data privacy, cybersecurity, and safety. Here are a number of them

Data Protection Regulations

This set of rules governs the collection, processing, storage and usage of data. The United States enacted the California Consumer Privacy Act (CCPA). The CCPA mostly applies to consumer IoT. However, it can also apply to industrial IoT if identifiable data is collected.

The European Union (EU) applies the General Data Protection Regulation (GDPR) to any Industrial IoT system that collects people’s personal data. For example, any smart systems that tracks worker movements must comply with:

  • Strong cybersecurity standards.
  • Right to fair consent.
  • Data minimization.
  • Right to access or delete data.

Cybersecurity Regulations

The IEC 62443 is a global benchmark used to secure Industrial Automation and Control Systems (IACS). It covers issues pertaining to Secure Development Life Cycle (SDLC), access control, device security and network security.

The United States NIST critical infrastructure regulations offer guidelines for smart grids, critical manufacturing and Industrial Control Systems (ICS). The European Union has the NIS2 directive, which focuses on essential industries like transport, energy and manufacturing. The NIS2 directive has laws pertaining to incident reporting, supply-chain security and cybersecurity.

IIoT Technical and Interoperability Standards

IoT technical standards define how sensors, computing units and actuators should communicate and transmit data to one another. One lightweight protocol used in IIoT is Message Queuing Telemetry Transport (MQTT). MQTT is perfect for remote and low-bandwidth sensors.

Open Platform Communications - Unified Architecture (OPC - UA) is the industry standard for machine-to-machine communication. It offers strong security features in terms of authentication and encryption. The ISO/IEC 30141 defines the reference architecture. It gives important information on the network layer, device layer, service layer and security layer.

Final Thoughts

Industrial IoT will continue to evolve and gain greater adoption in the decades to come. It is the key to digital transformation and more efficient supply chains. With the 5G network and the increased evolution of Artificial Intelligence, Industrial IoT will make humans more productive and efficient.

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