When your hand is burned, do you feel pain first or pull your hand away first?

Industrial manufacturing processes generate vast amounts of data, and leveraging this data can achieve important goals: predicting failures, optimizing equipment lifespan, and optimizing production processes to better meet market demands. The first step in any industrial network is data collection, followed by a balanced approach between local real-time data processing and long-term offline data storage, and finally, effective action to optimize industrial processes.

Information is collected and sent to a central location, and short IT outages are usually acceptable if the information is sent relatively early. However, as companies around the world become increasingly reliant on their information technology, the time they can take to maintain their equipment has decreased significantly, falling far short of the response time available with existing technology. Therefore, within advanced technology teams, modern IT systems use powerful AI and machine learning (ML) suites to enable their IT infrastructure to react more quickly to changes in sensor data reports. A previous report:

IIoT (Industrial Internet of Things) is the continuous integration of various acquisition (sensing) technologies with perception and monitoring capabilities, controllers, mobile communications, and intelligent analysis into every link of the industrial production process, thereby significantly improving manufacturing efficiency, improving product quality, reducing product costs and resource consumption, and ultimately achieving a new stage of upgrading traditional industries to intelligence.

Most of the network solutions for the entire industry are cloud computing solutions.

Edge computing refers to an open platform integrating core network, computing, storage, and application capabilities close to the source of objects or data. This platform processes and stores critical data locally before transmitting it to a central data center or cloud repository. Edge computing helps optimize cloud computing systems against data transmission-related interruptions. Cloud servers become control nodes for intelligent edge devices, performing summary analysis.

However, true edge computing doesn't stop there. With increasing demand, Industry 4.0 is no longer the focus of debate. The real question is how industry will accelerate the digital transformation of manufacturing. Promoting production optimization and reducing maintenance costs are the main drivers of actual investment in Industrial IoT infrastructure, as these facilities can deliver immediate, measurable benefits.

In the traditional Industrial IoT model, sensors or hardware collect data and transmit it to a higher-level IoT server or platform via built-in network connections. This data is then used for data analysis, visualization, and application development. Finally, management uses the results of these analyses to develop solutions, perhaps for machine maintenance, production process optimization, or other areas.

However, most data is real-time and does not need to be transmitted to upper-level servers. As McKinsey said, the need to move data processing to the edge of the network and the ever-increasing processing power requirements have created a different type of industrial Internet of Things network - this network may not have a strict hierarchical structure, and will reflect a variety of connection and processing methods in many forms of edge devices.

Cloud computing has been increasingly used in industry, and edge computing can help it handle problems more conveniently. So when is edge computing essential?

While the intelligent and fuzzy nature of the edge layer has significantly improved efficiency, it also presents significant security risks. For example, a lack of standards and specifications makes it difficult to ensure security quality; edge devices are exposed to the internet while industrial networks are closed; and edge devices break the constraints of centralized security management, inevitably leading to easily exploitable security vulnerabilities. However, embedding computing into edge devices makes sense, as it can mitigate the time and financial costs associated with latency. Even though industrial networks are initially integrated with centralized management, the future trend remains towards differentiated development. Edge computing also provides equipment vendors with an opportunity to sell a large number of new software, hardware, and solutions, and many software and chip vendors are actively pursuing this strategy.

Only products with similar development trends are likely to emerge as winners. The differentiation of software and hardware, the reduction of coupling, and the shift from cloud computing to edge computing, from convergence to differentiation, are the inevitable laws of development. To date, the most successful converged product is probably the mobile phone, driven by convenience. Other products, after initial convergence, have gradually diverged, driven also by convenience, but sometimes more so by cost reduction.