The trend to move process data directly from the factory floor to the digital cloud creates bandwidth and latency issues that can become a roadblock to real-time reporting. In response, smart sensors were introduced to efficiently perform computing at the “edge” of the factory network.
Edge computing is the practice of processing data near the edge of your network, where the data is being generated, instead of in a centralized data-processing warehouse. Edge computing is a distributed, open IT architecture that features decentralized processing power, enabling mobile computing and Internet of Things (IoT) technologies. In edge computing, data is processed by the device itself or by a local computer or server, rather than being transmitted to a data center. – (Source Hewlett Packard)
Edge devices reduce data loads and report meaningful, high-level results that supply real-time information. This approach allows industrial organizations to analyze essential data faster by processing machine-based data closer to the source.
Industrial machines, industrial controllers, data processors, and time-series databases that aggregate data from a network of equipment and sensors are all examples of early edge technology.
Cloud vs. Edge Computing
To illustrate the difference between cloud and edge systems in the Cloud paradigm, a camera sends a photo of a vehicle with a license plate to a server, and the server then reads the plate number. The server is responsible for data processing.
In the Edge paradigm, the camera reads the license plate number, then sends only the plate number to the server for identification. The camera is processing the data at the edge.
Benefits for Industrial Production
Edge computing dramatically reduces system complexity by providing customization of production modes. As a result, small-quantity and multi-batch models can replace high-volume manufacturing.
Multi-model and batch methods are leaner and yield higher quality products, with fewer errors, less downtime due to cell and tooling changeover, as well as reduced waste. Also, flexible adjustments to production plans and rapid deployment of new processes and models offer significant cost benefits to smart manufacturers.
Smart sensors are a vital component of edge systems, providing a range of abilities that improve productivity in the factory and assist in driving Industry 4.0 and IIoT.
Smaller Form Factor
Production systems now require smaller and more autonomous edge devices (Smart Sensors) that can run for extended periods of continuous use at the perimeter of the network. Compact smart sensors increase ease of system integration and device usage—both beneficial to edge system deployment.
In addition to being compact and ruggedly constructed, edge devices need to be network aware. Smart sensors address this need by having an IP address and support network communication protocols that enable direct communication with other factory equipment such as robots and PLCs, or for the transfer of measurement data to factory databases.
In a network-enabled smart sensor that digitizes and measures a target object, smaller packets of high-level data are communicated to the cloud at select intervals—rather than transferring raw scan data continuously for processing elsewhere. This capability alleviates pressure on network connectivity and minimizes latency and bottlenecking, all by providing distributed processing on the edge of the factory network.
Configuration and maintenance of many production systems leveraging smart sensor technologies can lead to network management challenges. This challenge is why edge computing requires all devices in the network be handled in a uniform manner—ideally as a fully automated process controlled with built-in software.
In a smart paradigm, the smart sensor cannot only acquire data but also process data and communicate control decisions to factory equipment—directly from the edge, without having to send data back to a centralized location or local computer racks. The software allows smart sensors to carry out computing and storage on-board so that select applications can be executed locally at very high speeds.
Distributed and Scalable Network Architecture
Edge computing relies on the seamless cooperation of distributed peers. There is no centralized controller anymore; instead, a collection of devices acting independently yet coory, proprietary information can easily be leaked through any connected device, platform, or even the network itself. Consequently, manufacturers are becoming more and more concerned about the exposure risk to their proprietary data.
In addition to the technical challenges introduced by a large number of devices running in a factory, there is now another demand for smart sensors to support mmunicating cooperatively.
Smart sensor networks built on a distributed architecture facilitate scalability and empower process engineers to develop specific measurement and control solutions for each manufacturing cell. Applications are implemented by using “snippets” of code that can securely run edge devices, requiring minimum interaction with coordinating elements. This prevents unnecessary or undesired uploads to data-center located servers.
Security and Privacy
In today’s factsecurity such as user profiles (eg., technician vs. administrator), encrypted firmware and settings, and secure protocols to exchange data with cloud storage.
Edge computing improves time-to-action and reduces latencies down to milliseconds while minimizing network bandwidth. In combination with cloud computing and powered by smart sensor technology, these edge systems can have a profound impact on industrial system performance and ultimately increase productivity and profit for manufacturers.
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