Edge computing and industrial IoT: why process data close to the machines

Edge computing brings data processing closer to its source. A lever for performance, sovereignty and responsiveness in industry. Edge computing: what is it?: Edge computing processes data at or near its source (machines, sensors, cameras) rather than sending it to the cloud. In industrial settings, this means installing computing capacity directly in the factory. Result: latency reduced to milliseconds, operation even without internet, and sensitive data that never leaves the site. Industrial IoT: the explosion of field data: A modern factory generates 1-5 TB of data daily via sensors. Sending everything to the cloud is costly and sometimes impossible. Edge computing filters, aggregates and analyses data locally, only sending relevant information to the cloud (10-20% of initial volume). Industrial use cases: Predictive maintenance: embedded ML models analyse machine vibrations in real-time. Visual quality control: cameras with embedded AI inspect parts at 100%. Energy optimisation: real-time consumption analysis per machine. Security: intrusion or dangerous situation detection via local video analysis. Technologies and architectures: Hardware: Nvidia Jetson, industrial Raspberry Pi, IoT gateways. Software: Docker containers on edge, lightweight Kubernetes (K3s). Protocols: MQTT for sensor-edge communication, OPC UA for industrial machines. Hybrid architecture: edge for real-time + cloud for historisation and model training. Edge computing and sovereignty: Edge computing is a natural ally of digital sovereignty: data doesn't leave your site, you control end-to-end processing, and you're not dependent on a cloud provider.