Edge computing means placing compute, storage, and sometimes AI inference close to the device or environment that produces the data. That edge may be the device itself, a local gateway, a factory server, a vehicle computer, or another nearby system rather than a distant cloud region.
Why It Matters
Edge computing helps when latency, bandwidth, resilience, or privacy matter. If a camera, robot, machine, or home hub has to wait on a round-trip to a remote data center, the system may be too slow or too fragile for the job. Local processing lets the system keep working when the network is weak and makes real-time response more practical.
How It Relates To AI
Edge computing is broader than On-Device AI. On-device AI means the model runs on the device itself. Edge computing can also mean running models on a nearby gateway, plant server, or vehicle computer that serves many devices at once. In modern IoT systems, edge layers often handle filtering, inference, control logic, and telemetry before the cloud takes over for longer-term storage, coordination, or training.
Where You See It
You see edge computing in factories, stores, vehicles, utilities, hospitals, smart homes, and other deployments where devices have to keep responding even when network conditions are imperfect. It is one of the main reasons connected systems now feel faster, more durable, and more operationally credible than earlier cloud-only IoT designs.
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Related concepts: On-Device AI, Telemetry, Sensor Fusion, Predictive Maintenance, and Ambient Computing.