Edge Computing

A distributed computing paradigm where data processing occurs on devices at the network edge near data sources. In RTLS, edge computing processes tracking data locally providing ultra-low latency, reduced bandwidth requirements, improved reliability, and enhanced privacy. Particularly valuable for time-critical applications like collision avoidance and real-time AGV navigation requiring sub-second response.

Computing architecture where data processing occurs at or near data source (field devices, local servers) rather than centralized data centers or cloud. In industrial RTLS context, edge computing distributes intelligence: anchors or local gateways perform position calculations, filters, and time-critical analytics locally rather than sending raw data to central servers. Edge computing benefits for RTLS include: reduced latency (local processing eliminates network round-trips, achieving 10-100ms vs. 100-1000ms for centralized), continued operation during network outages (critical for safety functions), reduced network bandwidth requirements (transmitting computed results vs. raw measurements saves 100-1000x data volume), and improved scalability (processing distributed across devices rather than bottlenecked at central servers). RTLS edge computing typical architecture: local edge servers (industrial PCs with 16-32 GB RAM, multi-core CPUs) located in facility IT closets handle position calculation for 200-1000 tags, perform geofence monitoring and local alerting, and forward aggregated data to central systems for facility-wide analytics and historical storage. Safety-critical functions (collision avoidance, emergency response) always run at edge to ensure sub-second response independent of network connectivity. Edge computing essential for multi-site deployments where central coordination unnecessary and local autonomy preferred.