The useage of edge computing in the industrial sector

The industrial automation market is leading in the development of a number of new technologies, which include augmented reality (AR), 3D printing, robotics, artificial intelligence (AI), cloud-based dispatch control and data acquisition systems (SCADA) and programmable automation system controllers (PAC) . If we talk about automation technologies, from the production hall to the supply chain and the very heart of the enterprise, the Internet of Things is already connecting various points with smart sensors. The Industrial Internet of Things (Industrial IoT) provides information for maintenance, inventory, and transportation of products.

However, simply building up network connectivity and organizing data flows is not enough to truly exploit the potential of digital transformation. To gain a competitive advantage, manufacturing enterprises need to fully integrate industrial automation. Only in this case will they be able to convert the data collected in the IoT environment into valuable analytical information in order to provide faster, more accurate and economical decision making. And for this, they must transfer computing power to the edge of the network - to its periphery.

Benefits from Edge Computing to data centers

Peripheral computing is a kind of rocket fuel for IoT. They are characterized by a number of advantages and potential opportunities:

peripheral computing allows you to analyze and filter data closer to the sensors. Moreover, only relevant data is sent to the cloud;

a delay in the production process can be critical, for example, if a failure occurs on the production line. The short response time, measured in milliseconds, is critical to ensure the security of critical and accurate operations. In such cases, waiting for the result from the IoT cloud platform is too long;

Peripheral computing means that, if necessary, sensitive data can be processed in a place where it is protected from direct network connections. This provides a higher level of control over the security and confidentiality of information;

finally, the requirements for cloud storage capacity and network bandwidth are reduced, the corresponding costs are reduced, because instead of sending to the cloud a large amount of data from sensors can be processed directly on the periphery.