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Processing data close to the source (e.g. IoT devices) to optimize latency, bandwidth and privacy.
Edge processing means performing data processing close to the source (IoT devices, sensors, gateways) to reduce latency, bandwidth use, and improve privacy. It shifts analytics, filtering and decisions from central cloud datacenters to local devices or edge nodes. Suitable for real‑time analytics, offline capabilities and more resilient distributed systems.
Measured time from event to local decision response.
Amount of data sent to central infrastructure per time unit.
Percentage of uptime of edge components.
In a production line, vibration data is preprocessed at edge nodes and only anomalies are reported to the cloud.
Traffic data is aggregated locally and traffic‑light decisions are made decentrally to minimize latency.
Sensors send data to a gateway that performs offline analyses and only transmits aggregated results over variable mobile links.
Requirements analysis: define latency, privacy, data volumes.
Architectural design: determine what runs locally vs centrally.
Select and deploy edge nodes, container runtimes and orchestration.
Introduce monitoring, security configuration and regular updates.