Edgedriven

Edgedriven is an adjective used to describe systems, architectures, or strategies in which processing, decision-making, or control is performed at or near the edge of a network or system, close to data sources such as sensors, devices, or users, rather than primarily in centralized cloud data centers.

In practice, edgedriven design is associated with edge computing, fog computing, and edge analytics. Common applications include real-time video analytics on surveillance cameras, industrial Internet of Things sensors performing anomaly detection at the device, and autonomous systems that must respond locally without round trips to a data center.

Benefits of edgedriven approaches include reduced latency, lower bandwidth usage, improved privacy, and greater resilience to network outages. Challenges include managing the heterogeneity of devices, securing edge nodes, deploying updates, and ensuring data consistency across distributed components.

The term is not a formal standard; it is used descriptively in industry and academic writing to characterize architectures or strategies that emphasize edge-centric processing. It complements concepts such as edge computing, cloud computing, and distributed systems, and is often discussed in the context of modern digital infrastructure such as IoT, smart cities, and industrial automation.