Definition
Edge Computing is a distributed model of computing systems that brings data storage and processing closer to the location where it’s obtained. It enables the analysis of large volumes of distributed data, in real-time, in a cyber-secure and scalable way and allows the integration of field equipment across different vendors, technologies and protocols.
Benefits of Edge Computing
1. Increased cybersecurity
As more devices connect to IoT networks and collect more data, taking care of industrial security becomes paramount to protect any company’s critical data and processes.
Edge computing technology makes it possible to process all data locally, reducing the chances of finding vulnerabilities. When working with large amounts of data, it is extremely important to bear in mind this factor, especially knowing that nowadays information is as valuable as the service or the final product.
2. Lower latency
Edge computing allows companies to process and analyze large amounts of data from their own devices, shortening load times to milliseconds.
Edge technology allows us to virtually eliminate the latency barrier and operate in real-time. When a device detects a failure of any kind, we can act immediately. Otherwise, taking action would take longer, which would most likely result in greater losses.
3. Exponential scalability
Industrial companies can combine the use of local and remote networks in a flexible way, generating a scalable system according to their needs.
This is where one of the biggest advantages of edge computing for industry comes into play. It allows the IoT network to translate protocols so that different systems can work simultaneously.
4. Increased efficiency
The concept of efficiency could be defined as the ability to achieve results using the least amount of resources possible. Edge environments are very efficient because they leverage the use of the local network and minimize the use of resources such as bandwidth.
Edge computing offers a middle ground between on-premise and cloud installations. It analyzes the data we store locally and selects the most relevant information, such as process analysis results or machine status, that is then sent to the cloud. This decongests the cloud and reduces the overall costs associated with information storage.