In IIOT (Industrial Internet of Things) Edge Computing serves a vital role, as this fast processing and informative and automatic decision making assist with the continuous flow of the production line, while cloud and enterprise applications are mainly used for dictating procedures and long-periods of time monitoring & learning.
Edge computing is the practice of processing data closer to where it is generated – for example on the factory floor, where IO-Link Wireless is able to reach even the most complicated of places. IoT Edge Computing is an approach aimed at reducing latency, and generating faster decision making, rather than sending the information up to the cloud for processing and further instructions.
In IIOT (Industrial Internet of Things) Edge Computing serves a vital role, as this fast processing and informative and automatic decision making assist with the continuous flow of the production line, while cloud and enterprise applications are mainly used for dictating procedures and long-periods of time monitoring & learning.
In this blog post I’ll cover edge computing technology for Industrial IoT, showing how utilizing IO-Link Wireless technology enabled the creation of Emerson’s Energy Saver solution, in collaboration with AVENTICS’ technology – setting a new standard in sustainable industrial solutions.
Importance of Edge Computing
To understand its importance, and what is edge computing in IIoT Manufacturing, we must first understand the importance of edge computing on its own. For this, one must first understand the critical feature of data processing in production and manufacturing. While a certain status may seem perfectly good on the PLC, realistically, it can be optimized by a separate set of rules, running outside of it.
Let’s see such an example in IoT.
The Role of Edge Computing in IoT
The point in which IoT and Edge computing meet, can be found in the case of the Energy Saver application. Here, AVENTICS’ AF2 Air Flow Sensor and EV12 Pressure Regulator are each connected to CoreTigo’s IO-Link Wireless Bridge – TigoBridge, converting their built-in IO-Link communication into IO-Link Wireless. This allows to transmit real time data to CoreTigo’s IO-Link Wireless Gateway – TigoGateway 1TE (with the “E” standing for “Edge Computing”).
There the data from the AF2 Air Flow Sensor is processed on the factory floor, determining the required air pressure at the specific moment, and in many times, sending commands to the EV12 Pressure Regulator to reduce the pressure to the required minimum – thus saving energy and CO2 emission.
This Industrial IoT application demonstrates how grabbing real-time information, quickly processing it, and implementing a data-driven decision, contributes to the environment, as well as saves money, by reducing energy consumption.
Edge Computing vs Cloud Computing
While Cloud Computing serves as the “General Manager” of the industrial operation, seeing the full picture, and dictating orders, Edge Computing dives into the granular details, seeing the specific needs of each part of the process, able to make the changes required in each individual part.
In the example of the Energy Saver, for example, Cloud Computing would determine that the air pressure in all stations is the right one, and no changes are needed. The Edge Computing in a specific station can determine that as its operator went on a break, it can lower the air pressure to the minimum level, reducing energy consumption until work resumes.
Ultimately, these decisions will be reviewed in the high levels, assuring whether these settings should be preserved or changed, yet, each station’s ability to process its own data, and act on it, has proven to be very efficient. Thus, eventually it is not a matter of Edge Computing vs. Cloud Computing, but rather these two working together. When combined with IO-Link Wireless technology, new industrial wireless solutions are created.
Edge Computing vs Industrial IoT
Edge Computing and IIoT are related topics, serving a common purpose, yet they are distinct from each other. While the devices in an IIoT network interconnect to collect and share data, enabling Industrial automation across various environments, traditional IIoT systems often rely on cloud computing for data processing, causing latency and bandwidth issues.
Edge computing addresses these challenges by performing the data-processing closer to the source, on local devices or edge servers, thus reducing latency, and improving real-time & decision-making processes.
By integrating Edge Computing with the IIoT process in the case of the Energy Saver, this latency was spared, resulting in faster processes, and ultimately better results. Integrating the IIoT process with Edge Computing yields various benefits, as was in the Energy Saver case.
Benefits of Edge Computing in Industrial IoT
Edge Computing technology for Industrial IoT enables an array of benefits, assisting with optimizing processes and assuring the return on investment in IIoT solutions.
Real-time Latency Reduction
Avoiding sending the data all the way “up north” to cloud computing, generates quicker processing and responses in IIoT manufacturing. One must also keep in mind that the Cloud Processing tool receives data from various sources, causing latencies. Sending the processed data from Edge Devices (such as the TigoGateway 1TE) reduces the amount of data it needs to handle.
Optimizing Bandwidth Utilization
In respect to the former, reducing the data rates sent to the cloud assists with optimizing bandwidth utilization, as not only the amount of data sent decreases, but the timing in which it is sent can be dispersed. Not sending all of the data all of the time, but rather a smaller portion at pre-orchestrated time slots helps the information run smoothly.
Enhancing IIoT Security
Lowering the volume of data sent to the cloud also helps with security compliance. Sending less data over the ethernet, means less data can be intercepted or interfered with. Raw data is of great value for malicious purposes, therefore, sending processed data, revealing only fractions of the data along with the actions taken will be of less value for those wishing to harm the processes.
Enabling Scalability
Allocating and dispersing the data processing between Edge Computing devices allows scalability within the industrial facility. Since there’s a limit to the amount of data the cloud and enterprise applications can take in and handle at a specific time, conducting the data processing away from them, lowering the amount of data sent to them, and moderating the traffic to them – all allow adding edge units while assuring the higher applications can handle the data transmitted their way.
Edge Computing for Industrial IoT: Examples
Apart from the example of the Energy Saver application discussed and covered in this blog post, there are numerous other use cases where Edge Computing enhances IIoT solutions:
- Predictive Maintenance: Edge Computing allows improving and optimizing predictive maintenance processes, by continuously running condition monitoring algorithms on data received from machines and production lines.
- Quality Control: Edge devices can be used for monitoring the production process to ensure compliance with quality standards, e.g analyzing data from sensors, ensuring food products are processed according to industry and health standards.
- Autonomous Robots: In Autonomous Mobile Robots and other Mobile Equipment platforms, edge computing processes sensor data locally for real-time decision-making. This enables autonomous robots to adapt to changing conditions on the go.
Frequently Asked Questions about Edge Computing for IIoT
What is the edge architecture of Industrial IoT?
The edge architecture of Industrial IoT consists of data distributed nodes (such as IO-Link or IO-Link Wireless), and Edge Computing devices/servers (such as Gateways) that process and analyze data at or near the source.
How does Edge Computing affect industrial automation?
Edge Computing enhances industrial automation by enabling real-time data processing and decision-making directly on the production floor. This reduces latency, increases scalability, and more. Ultimately it improves operational efficiency by minimizing dependency on enterprise & cloud computing.
What are the disadvantages of Edge Computing?
Edge computing’s disadvantages may include higher initial costs for deployment, as well as increased overhead for managing and orchestrating distributed systems across multiple edge nodes.
Gabi is an experienced executive with over 20 years in the hi-tech industry and wireless technologies. He brings global experience in enterprise solutions from a variety of companies ranging from large corporates such as Intel and Stanley Black & Decker, in addition to start-up companies at various stages. Most recently, Gabi led the Marketing and Product strategy of AeroScout which pioneered the WI-FI RFID space and were acquired by Stanley Black & Decker. At Stanley, Gabi led the Solutions, Products, Business Development, and Marketing of the STANLEY Healthcare division serving over 10,000 global enterprise customers.
Gabi holds an Information Systems & Industrial Engineering degree (B.Sc with honors) and an MBA from the Ben-Gurion University.
