Lately, it feels like new technologies have arrived to refine or address a missing piece of the SCADA puzzle. Despite SCADA emerging as a control system in the 1950s—a considerable time ago—it continues to evolve with the integration of these advancements. Meaning, it’s very unlikely SCADA will die.
One of the technologies that keeps enhancing productivity in SCADA (and the main topic of this blog post) is edge computing. This technology gained prominence in the 2010s with the low latency requirements from smart cities, industrial automation, augmented reality, and autonomous vehicles.
But what exactly is this edge computing, and how does it fit into the SCADA landscape?
Well, we’ll answer that question and more. In this blog post, you’ll learn:
- What is edge computing?
- How does edge computing work?
- The difference between data storage in traditional SCADA and edge computing
- Advantages of using edge computing in SCADA systems
- Considerations for implementing edge computing on SCADA
In essence, SCADA conducts the grand “orchestra” of industrial processes, while edge computing responds instantaneously to subtle cues, enhancing the performance of your industrial equipment.
Now, let’s delve into a more detailed explanation.
What Is Edge Computing?
Edge computing is a model for computing and handling data, in which data is stored near the source to achieve a quick response time. These sources can be a device, sensor, or local server.
In computing, “edge” is the point at which a network generates or consumes data. This point is often closer to where data is generated by the source. From there stems its name, edge computing.
Edge computing is especially valuable for instances in which you have multiple devices that generate large amounts of data. With technologies like IoT (Internet of Things) for example, edge computing can ease the use of network bandwidth, reducing the time it takes for information to travel back and forth between the source and a centralized server.
How Does Edge Computing Work?
You already know the basics.
Edge computing processes data closer to where it’s generated. This reduces the need to send it to centralized servers, minimizing data travel and latency. As a result, you have real-time or near-real-time analysis for decision-making.
Now, here’s a step-by-step explanation of how edge computing works in the context of an industrial plant from the oil and gas sector:
Figure 1: Diagram of Edge Computing
Step 1: Collection at the Source
In an industrial plant, you have multiple devices or sensors that measure temperature, pressure, or flow rates from pipelines and machinery. These devices or sensors carry out their functions and collect data on these parameters.
Step 2: Proximity-Based Computing Resources
Computing resources (edge servers, gateways, or edge devices) are strategically placed near critical points in the plant. Pumps, valves, and processing units are critical points as that’s where the data is generated, and you want your edge devices to process this data in close proximity.
Step 3: Real-Time or Near-Real-Time Data Processing (Locally)
The edge servers use algorithms and machine learning models to process data locally and in real-time (or near-real-time). For instance, these models analyze pressure, temperature, or flow rate data to ensure your equipment is operating within established constraints.
Step 4: Insights for Immediate Decision-Making
Let’s say that you have a temperature sensor to monitor the temperature at the flare. If the temperature drops below a certain predefined value, it could be a signal that the flame in the flare has extinguished.
When the temperature sensor detects this drop, an on-site camera is activated and takes a picture showing the current state of the flare. This picture is compared to a reference or baseline image. Finally, if it’s confirmed that the flame is out an alert is generated.
Step 5: Central Cloud Storage for Posterior Analysis (Optional)
As an option, you can send relevant data and critical alerts to a central cloud server. This server can work as a repository for long-term storage and analysis. For example, this data can be used to predict trends and optimize maintenance schedules.
Data Storage: Edge Computing vs. Traditional SCADA System
So, what’s the difference in data storage between edge computing and traditional SCADA systems?
Well, the main difference lies in how and where the data is stored and processed.
Edge computing brings computation closer to where the data is generated. But its focus isn’t on storing data, it’s more about real-time processing and decision-making. What’s more, edge computing isn’t limited to handling industrial data. It processes any kind of data such as time series, video, images, sensor data, and more.
SCADA stores data in a centralized location within your industrial facility or offsite data center. Its main purpose is to store data generated by industrial processes and machinery to use it for historical tracking, monitoring, and posterior analysis. SCADA systems (predominantly) deal with time-series data like sensor readings.
Advantages of Edge Computing in SCADA Systems
Integrating edge computing into SCADA systems can offer several significant advantages. Here are five main benefits:
- Reduced Latency for Real-Time Decision-Making: Edge computing minimizes data travel and processing time, enabling instantaneous responses to critical events in industrial processes.
- Enhanced Reliability and Availability: Edge devices operate independently, ensuring continued monitoring and control even during network disruptions, which improves system reliability.>
- Bandwidth Efficiency and Network Optimization: By processing and transmitting only relevant data, edge computing conserves network bandwidth, reducing congestion and enhancing overall network efficiency.
- Augmented Security and Privacy Measures: Edge devices process sensitive data locally, providing an extra layer of security and safeguarding against potential security threats.
- Scalability and Flexibility for Growing Demands:Edge computing allows for easy scalability by adding more edge devices. This flexibility accommodates changing requirements and optimizes resource utilization.
As a side note, edge devices can run suitable software like Node-RED and Ignition Edge, which enhance their capabilities. Node RED provides a visual programming tool, while Ignition Edge offers industrial data solutions, expanding the range of tasks these devices can perform. (You can read our article about Node-RED here.)
Implementation Considerations for Edge Computing in SCADA Systems
Here are the specific considerations for implementing edge computing in SCADA systems:
What type of hardware or infrastructure do I need to integrate edge computing with SCADA?
To integrate edge computing with SCADA, you’ll need:
- Edge Devices: Opt for robust edge devices equipped with sufficient processing power, memory, and storage capacity. Strong devices are recommended for harsh industrial environments to ensure reliability. Also consider compliance requirements associated with deployments in hazardous areas such as inflammable gases or vapors that can generate flammable substances. For example, the device may need to be installed in an explosion enclosure to comply with Class 1 Div 2 requirements.
- Networking Equipment: Ensure low-latency, high-reliability network connectivity between edge devices and the central SCADA system. Industrial-grade switches and routers may be necessary.>
What algorithms and models do you need?
You need to select algorithms and models tailored to your industrial processes like:
- Real-time Processing Algorithms: Use algorithms designed for rapid data processing at the edge. For example, implement filtering, aggregation, or machine learning models for immediate analysis.
- Anomaly Detection Models: Employ algorithms capable of quickly detecting anomalies in sensor data. These models can trigger immediate alerts for critical events.
What recommendations for security and compliance should you consider?
You should implement robust security measures to safeguard your edge computing implementation like:
- Access Control Policies: Define strict access controls to ensure only authorized personnel can interact with edge devices. This includes strong authentication mechanisms and role-based access.
- Data Encryption Protocols: Use encryption protocols (such as TLS/SSL) to secure data both in transit and at rest on edge devices, safeguarding it from unauthorized access or interception.
- Compliance with Industry Standards: Adhere to industry-specific regulations and standards governing data privacy and security. This includes compliance with standards like NIST, ISO 27001, and industry-specific frameworks.
Edge Computing in SCADA: Enhancing Real-Time Processing
In the realm of industrial control systems, the convergence of SCADA and edge computing marks a milestone. It reaffirms the enduring nature of technological evolution. Picture SCADA as the conductor, directing operations, while edge computing stands ready to interpret nuanced cues in real-time.
This integration isn’t just about efficiency; it’s about tradition and innovation combined to create cutting-edge solutions.
Need an Edge?
Do you want to implement Edge Computing into your SCADA system?