Industry 4.0 is expected to be one of the largest areas for future infrastructure investments to improve and build a working modern factory that is in sync with the advanced technology we use every single day. Every modern production facility is being built today with a large network of connected devices to monitor and relay signal data coming from every single process for improved quality control, process optimization, and waste reduction.
Traditionally, behind the connected sensors in a smart factory were edge devices that served as a gateway to transmit data offsite for aggregation and analysis. Central servers could make centralized actions for process control but an improvement to this method would be an evolution of the edge device to reduce reliance on cloud processing. By improving the performance and capability of edge computers, new innovative applications can be applied. The increase in localized processing and performance means the breakthrough of self-driving vehicles, advanced signal processing and full factory automation.
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Premio’s RCO/VCO-6020-1050ti are industrial GPU embedded systems that serve as powerful edge computing machines utilizing GPU processing cores for increased workload applications. This includes some of our real use case capabilities in computer/machine vision systems featuring facial recognition for airport security and computed tomography for full 3D baggage scanners. By upgrading edge gateway devices into edge computing systems, then Industry 4.0 applications have access to the following advantages:
1. Improved response time
By enabling the system to perform computation intensive tasks such as machine vision or AI locally, we can greatly reduce overall response times. The unit no longer needs to gather the data and transmit it via the cloud for processing and then wait for the returning results. Data can now be processed locally with real-time results to improve overall efficiency in a factory environment.
2. Enhanced security
One of the main tenets of network security is to store data locally whenever possible. Whenever data needs to be transmitted, either by wire or over air, there is a chance that data may be intercepted. Even if the data is strongly encrypted, an attacker can monitor the frequency and amount of data transmission; from there, the attacker can infer the purpose of the device and utilize that knowledge to attack other susceptible components on the network. By keeping the data locally, this can be used to prevent man-in-the-middle attacks, where an attacker places himself in the middle between the device and the cloud. This enables the attacker to monitor all data flow and even possibly modify or inject malicious data into the stream.
3. Network traffic reduction
By greatly reducing the amount of data transmitted via the cloud, an intelligent edge system can help to improve overall network congestion and latency. As the demand of the quality of data (1080P, 4K, or even 8K video stream) scales up, the amount of network traffic may overwhelm even the most robust network infrastructure. The solution may not be spending more on adding additional network capacity, but finding an economical method to reduce overall network traffic.
4. M2M communication
Machine to Machine communication allows devices to communicate with each other without human intervention. But in order to do this, a device requires sufficient intelligence and processing power to analyze any given task and relay the results to another system without reliance on the cloud. By completely eliminating the need of any type of human interaction, the system's response time is greatly improved; individual devices are then able to operate and interact with each other autonomously in real-time.