Advantages of Edge Computing
Definition: Edge Computing is a technology architecture that operates locally, within-or very close to-where physical or digital processes occur.
Best for: Real-time robotics, vision inspection, safety systems.
Ultra-Low Latency (The "Real-Time" Factor): In automation, a robot arm requires information on its next steps within milliseconds. Sending that video feed to the cloud, processing it, and sending a command back takes too long. Edge Advantage: Processing happens on the device (a local server), enabling response times that can be under 10ms. This is critical for safety stops and high-speed pick and place and sorting.
Bandwidth Efficiency (Cost Savings): A single high-definition machine vision camera can generate and communicate enormous amounts of data. Uploading terabytes of raw video to the cloud in an ongoing basis is prohibitively expensive and clogs network pipes. Edge Advantage: The edge device processes the images or video, discards the "normal" frames, uses and stores what it requires to make the automation decision it communicates to the robotic controller.
Reliability & Offline Operations: Facilities cannot stop production just because the internet connection drops. Edge Advantage: Edge devices are autonomous by definition. A sorting cell, for example, will continue to classify and sort products even if the connection to the central server is severed.
Security & Data Sovereignty: End users of automation are often hesitant to send proprietary production data (like trade-secret formulas or cycle times or product data) off-site. Also, opening system ports to the outside world sometimes poses security risks that organizations don’t want to take. Edge Advantage: Sensitive raw data never leaves the facility floor, and access is limited to local networks. Only anonymized insights are transmitted externally.
Advantages of Cloud Computing
Definition: Cloud computing is the on-demand delivery of technology resources (servers, storage, networks, applications) over the internet, remote from physical location where services are consumed.
Best for: Fleet management, predictive maintenance, training AI models, long-term data & process analysis.
While edge computing is better for immediate, local action, the cloud is superior for big picture or multi-site (read: global) tasks.
Infinite Scalability: Store high volume of historical sensor data to spot long-term trends
Model Training: Training a complex vision model requires massive GPU power that can be prohibitively expensive to put on every edge device. Fizyr OS trains in the cloud, then we deploy updated neural networks to edge resources. Technology and capabilities are improving in this area, and in the future more 'reinforcement learning' models will be able to perform in edge computing environments.
Global Visibility & Monitoring: A manager can see the performance of, or issues with, multiple automation instances and facilities on a single dashboard when edge devices are interactively reporting to a central, cloud-based hub.
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