Edge Computing

» What is edge computing?
» How does edge computing work?
» What are the benefits of edge computing?
» How does edge computing differ from cloud computing?
» What are some examples of edge applications?
» The main security risks when using edge computing
» What are the different approaches to edge computing?
» What challenges exist in implementing edge computing?
» Applying edge computing in your production environment, what do you need?
» Edge computing and Ignition
» Explore Ignition

What is edge computing?

Edge computing means that computers and devices that collect and process data are placed closer to each other, rather than in a central location far away. This allows for faster data processing and the ability to make real-time decisions. As a result, data doesn’t need to travel far to a central location, saving time and energy.

The application of edge computing is particularly valuable for industries where it is important to make quick decisions based on data, such as in healthcare, manufacturing, and transportation.

How does edge computing work?

Edge computing works by bringing computing capacity and storage closer to the source of the data, rather than keeping it centrally in a data center or cloud. This architecture utilizes local devices and systems such as routers, switches, gateways, smart sensors, and IoT (Internet of Things) devices to process and analyze data at the location.

When sensors in a factory collect data, this data is immediately sent to the edge of the network. There, a local server or gateway processes this data. The analysis of the data takes place immediately, allowing for real-time decision-making. This enables faster and more efficient data processing, which is particularly important in situations where speed and real-time decision-making are essential.

Edge computing also reduces the amount of data that needs to be sent to a central location. This reduces network congestion as well as data traffic. This leads to better performance, less network latency, and better data security.

What are the benefits of edge computing?

• Faster data processing: By bringing capacity and storage closer to the source of the data, edge computing processes data much faster than traditional cloud computing.
• Real-time decision-making:It enables making decisions based on real-time data. This allows businesses to respond faster to changing conditions and make better decisions.
• Lower network load: It reduces the amount of data that needs to be sent to a central location, reducing network load. This leads to better performance and less network traffic delay.
• Better data security: Processing data at the edge of the network reduces the distance the data travels, making it less susceptible to data theft or leaks.
• Cost savings: Sending data to a central location is no longer necessary. This leads to lower costs for businesses.
• Improved scalability: It allows scaling data processing based on organizational needs. This enables businesses to easily expand as their needs grow.

How does edge computing differ from cloud computing?

Both are technologies for data processing and storage, but there are some key differences between them:

• Location: Edge places capacity and storage close to the data source, while cloud uses centralized data centers to store and process data.
• Processing time: Edge processes data much faster because data processing occurs locally. Cloud needs to send the data to a central location before processing takes place.
• Bandwidth: Edge reduces the amount of data that needs to be sent to a central location. On the other hand, cloud requires significant bandwidth to move large amounts of data between edge devices and the central data center.
• Scalability: Edge is scalable based on the needs of an organization, while cloud depends on the scalability of data centers.
• Cost: Edge lowers costs by reducing the need to send data to a central location. Cloud often incurs higher costs due to the need for extensive infrastructure and maintenance of data centers.

What are some examples of edge applications?

In general, every industry benefits from edge computing if they want to optimize data processing and analysis, deliver fast and reliable services, and reduce costs. Some examples include:

Industrial machinery, such as robots and sensors, perform real-time data processing and analysis to optimize performance and minimize downtime.

But also smart devices in your home, such as a smart thermostat, process data and perform actions without requiring a constant internet connection.

Mobile apps process data and perform actions on users’ devices, such as real-time voice and face recognition, which are not dependent on a constant internet connection.

Autonomous vehicles, such as self-driving cars, process real-time data, such as images from cameras and sensors, to control the vehicles and ensure safety.

The main security risks when using edge computing

By gathering data close to the source, maintaining security may be more challenging because these systems are often less controlled and less secure than centralized cloud infrastructure. However, there are ways to enhance security, such as using encryption, implementing firewalls, and establishing secure connections.

Furthermore, edge computing also offers security benefits. For example, by processing data and computations locally, businesses better protect personal data from unauthorized access and hacks. It also aids in threat detection and enables the implementation of security measures in real-time, rather than waiting for data processing in the cloud.

In summary, while edge computing presents security challenges, there are also ways to address these challenges and improve security. It is crucial for businesses to implement the appropriate security measures to ensure that their edge computing systems are and remain secure.

What are the different approaches to edge computing?

There are several approaches to edge computing that can be used depending on the specific application and use case. Some common approaches include:

Fog computing
Instead of processing data on devices very close to the source, fog computing utilizes small computers or servers located closer to the edge of the network.

Edge servers
In this approach, servers and storage devices are placed closer to the edge, facilitating faster data processing and storage. They also act as gateways between edge devices and the cloud, optimizing data flows.

Edge devices
Devices process and store data themselves, without the intervention of a server or cloud. This application is particularly important when latency is critical and when speed is crucial.

Hybrid approach
The use of both edge and cloud computing to process and store data.

What challenges exist in implementing edge computing?

Although edge computing offers many benefits, there are also some challenges when implementing it:

• With many devices involved in edge implementations, managing these devices may be challenging. It’s important to have a robust device management system in place to efficiently manage and maintain devices.
• Implementations may introduce vulnerabilities as data storage and processing occur at various locations. It’s crucial to ensure that all devices and networks are secure and compliant with information security standards.
• Data processing and storage need to be fast and efficient. Managing latency is essential to ensure that processing speeds meet application requirements.
• Implementations involve different devices, protocols, and systems. Ensuring that these components work well together and are interoperable is important.
• Scaling and expanding edge systems as application requirements change may be challenging. It’s crucial to have a robust and scalable architecture that can easily expand as demand grows.
• Acquiring devices, networks, and software incurs costs. It’s important to have a clear understanding of these costs and the ability to manage and control them.

Applying edge computing in your production environment, what do you need?

If you want to implement edge computing in your production environment, there are a few things you’ll need:

• Edge computing hardware: You need equipment capable of processing and storing data at the network edge. This includes a variety of devices such as edge servers, industrial PCs, or advanced edge devices like smart sensors and actuators.
• Network connectivity: You need a network connection between the edge devices and the central network. Depending on the application, this can be a wired or wireless network.
• Edge software: You need software designed for edge computing, such as device management software, data management, and data processing. This software should be installable and configurable on the edge hardware.
• Security solutions: You need to implement security solutions to protect data and equipment at the edge. This includes network security, access control, authentication, and data encryption.
• Human expertise: You need people with the right expertise to design, implement, and maintain edge solutions.

Edge computing and Ignition

Ignition is a software platform for industrial automation and IIoT-solutions (Industrial Internet of Things). Edge computing can be used in conjunction with Ignition to process and analyze data at the network edge before it is sent to the cloud or central network. This improves system performance by reducing the load on the network and cloud, as well as reducing latency and improving response time.

Ignition supports integration with various edge devices and platforms, including Raspberry Pi, Ignition Edge, and the Canary Labs Canary Historian. This allows for data collection and analysis at the network edge, even in environments with limited network connectivity.

By combining edge computing with Ignition, organizations benefit from real-time data analysis and processing, improved system performance and scalability, and enhanced data security and privacy.

Why implement edge computing with Ignition?

• Flexible and scalable platform that can be customized to the specific needs of a production environment. It is easy to expand to multiple locations and adapt to the specific requirements of a production process.
• Comprehensive capabilities for processing and analyzing data, enabling real-time insight and decision-making. The platform allows for complex analysis and intuitive data visualization.
• Easy integration with existing systems, such as ERP, MES, and SCADA systems. This enables data collection and integration from different systems and processes in the production environment.
• The extensive security and backup capabilities make it a secure and reliable platform. This is especially important in a production environment where downtime and data loss are costly.