Through the Industrial Internet of Things (IIoT) to implement automated systems, deploy sensors to measure, monitor, and analyze data, thereby improving efficiency and increasing revenue from production operations. More and more manufacturing companies are benefiting from this.
Fog computing is designed for data-intensive, high-performance computing, and high-risk environments. Fog is an emerging distributed architecture that bridges the cloud and the devices connected to it, without the need to establish a permanent cloud connection between the site and the factory. By selectively transferring computing, storage, communication, and control, fog computing can make decisions close to IoT sensors and actuators (this is data generation and use). It is a useful supplement to cloud computing, not a complete replacement so that IIoT can be used efficiently, economically, safely, and constructively in a manufacturing environment.
Fog is sometimes called edge computing, but there are key differences between them. Fog is a superset of edge functions. The fog architecture combines resources and data sources with a hierarchical structure that resides on north-south edge devices (cloud to sensors) and east-west edge devices (function-to-function or point-to-point) for maximum efficiency. Edge computing is often limited to a small number of north and south layers, usually related to simple protocol gateway functions.
The fog node is the basic element of the fog architecture. The fog node can be any device that provides the computing, network, storage, and acceleration elements of the fog architecture. For example, industrial controllers, switches, routers, embedded servers, complex gateways, programmable logic controllers (PLCs), and intelligent IoT nodes (such as video surveillance cameras).
The fog architecture that benefits the factory
Factories can make full use of the data flow of the fog node layer to make the connection between factories better. Fog nodes located at a lower level in the overall structure, such as a single computer, can be directly connected to local sensors and actuators to enable a timely analysis of data and interpretation of abnormal operating conditions. If it has been authorized, it can also respond and compensate for problems or solve problems autonomously. In addition, fog nodes can also send appropriate service requests for higher-level fog hierarchies to providers with better technical resources, machine learning capabilities, or maintenance services.
If the operating conditions require real-time decision-making, such as shutting down the equipment before it is damaged or adjusting key process parameters, the fog node can provide millisecond delay analysis and operation. Manufacturers do not have to use cloud data center routing to implement this real-time decision. This helps avoid potential delay issues, queue delays, or network/server downtime, which can cause industrial accidents, reduce production efficiency, or product quality.
In the factory, the fog nodes located at a higher level can obtain a broader perspective on industrial processes. They can add more functions, such as the visualization of production line operations, monitoring the status of malfunctioning machines, adjusting production parameters, modifying production plans, ordering supplies, and sending alerts to the right people.