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In 2015 edge computing gained its formal definition as an enabling technology allowing computation to be performed at the edge of the network, and from that point, businesses and solution providers considered and applied this technology toward many industrial applications.
We analyzed Industry 4.0 applications and solutions extensively and In this article, we will discuss how edge computing is implemented in industrial IoT solutions and the benefits for implementing it.
What is Edge computing?
In simple terms, Edge computing means performing some portion of the application algorithm on the devices near the data source instead of relying totally on a central data centre. Rather than transmitting raw data to a server for processing and analysis, that work is, instead, performed where the data is actually generated -- whether that's a retail store, a factory floor, a sprawling utility or across a smart city. Edge computing is a general term that encompasses lots of technologies and practices. In this article, we focus on Edge computing related to IoT solutions.
How is Edge computing implemented for industrial solutions?
During our work in an EU project that aimed to fill the gap between industrial IoT challenges and the technical skills of the workforce, we analyzed many industrial challenges, how they are solved, and what are the best technologies for solving them.
Industrial IoT solutions are diverse, but the two most common applications are:
- Real-time Monitoring and Supervision of an industrial process.
- Predictive Maintenance on components and machinery.
For Real-time production Monitoring applications, edge computing algorithms usually consist of :
- Pre-processing, filtering and formatting of data gathered from the sensors or devices. For simple applications, this processed data will be sent to the server where it will be visually displayed to the end-user through dashboards.
- For complex applications such as detecting the anomalies of the power consumption or detecting and predicting the state of the machine based on the machine’s performance, the edge computing algorithm consists of deploying and running machine learning models that analyze the data and make decisions or predict values.
For example, In a project we did with a major manufacturing company that produces automotive parts, by using our industrial data acquisition unit 4ZeroBox, we were able to monitor the production flow and predict the malfunction of an important valve that controlled the production process. These algorithms ran on the 4Zerobox.
Predictive maintenance solutions also use machine learning and AI algorithms at the edge layer. Predictive maintenance is the process that helps companies eliminate preventive maintenance and predict a component’s malfunction and lifecycle through AI and ML models.
Are there benefits to using edge computing algorithms in these applications?
- Latency is simply defined as the time between an event and the response for that event. In many industrial use cases, the total latency, which includes the processing and the return of the data to make a critical action, is important. Consider the case of an electronic barrier (also called a light curtain) around a welding machine. If anyone breaches the barrier, the welding turns itself off.
With remote work increasing plus digital platforms and services being leveraged more than ever before, edge computing is the solution for quick and reliable data processing. Edge computing algorithms have an advantage over central processing algorithms because they can reduce potential latency problems.
For that application, using a central server that analyzes the data and sends back an appropriate action adds latency time to each action. This latency build-up could produce adverse effects in critical operations.
- With IoT edge computing devices and edge data centres positioned closer to end-users, there is less chance of a network problem at a distant location that can affect local customers. Thanks to its decentralized algorithms, the devices continue to operate even if the connection to the cloud is disconnected.
Having a versatile and reliable network is very important because it adds a degree of stability to the system. As a user, you can be sure that the solution is running and executing the algorithms on the edge devices rather than depending on the connectivity and availability of the central servers.
- Even though the proliferation of IoT edge computing devices does increase the overall attack surface for networks, it also provides some important security advantages.
Generally, since more data is being processed on local devices, edge computing also reduces the amount of data actually at risk at a single moment as you don’t need to stream the data to servers. The users have full control and access to this data. From a security point of view, this is a big feature for solutions that have critical data.
- Edge computing is also better for the environment. Adopting edge computing technologies on a large scale have a great impact on the environment. Not only less bandwidth is used to send data to the cloud but also now central servers do not need the conventional enormous resources to run algorithms.
Are you interested in learning more about Edge computing technologies?
Zerynth and RS Components have created an exclusive IoT Edge Kit and Workshop package that is tailored for self-learners.
The workshop covers the basics of Industrial IoT, which includes the basic infrastructure of an IoT application whilst also providing practical experience on data gathering from industrial machines, sending this data to the cloud, and monitoring data in real-time.
Additionally, the workshop discusses the fundamentals of the IoT Cloud service, communication protocols, edge computing, plus the best practices in applying over-the-air updates.
To take part in this workshop head over to RS and purchase the workshop kitthen use the exclusive code supplied with the kit here on DesignSpark to access the workshops.