Latency

The time delay between an event occurring (tag moving, entering zone) and the system detecting and responding to that event. Critical for time-sensitive applications like collision avoidance, automated control, and safety alerts. Affected by factors including positioning calculation time, network transmission delays, and processing overhead. Edge computing reduces latency for critical applications.

Time delay between real-world event and system response, critical performance parameter for industrial RTLS applications. RTLS latency components include: (1) Tag transmission delay - time from physical movement to tag transmitting position signal (determined by update rate: 1 Hz = 1 second worst-case delay, 10 Hz = 100ms). (2) Propagation delay - signal travel time from tag to anchor (negligible, ~1-10 microseconds for 100m distance). (3) Processing delay - time for position calculation (typically 10-100ms depending on algorithm complexity and computational resources). (4) Network delay - time transmitting data from field devices to processing servers and application layer (1-50ms local network, 100-500ms cloud). (5) Application response delay - time for business logic execution and action triggering (10-500ms depending on complexity). Total system latency ranges: 100-500ms for local high-performance systems (edge processing, 10 Hz update rate), 0.5-2 seconds typical industrial systems (1 Hz updates, centralized processing), 1-5 seconds for lower-performance systems (0.1-1 Hz updates, complex processing, cloud communications). Latency requirements vary dramatically by application: collision avoidance requires under 200ms (for 2 m/s vehicles to stop within 40 cm), geofence monitoring tolerates 1-3 seconds (person walking 1.5 m/s travels 1.5-4.5m during alert delay), process tracking accepts 5-10 seconds (confirming completion or dwell times), analytics accepts minutes (trending and reporting non-time-critical). Reducing latency requires: higher tag update rates (increasing power consumption and system capacity requirements), edge processing (performing calculations locally vs. centralized servers), optimized algorithms (faster computation at possible accuracy cost), and local networks (avoiding WAN/cloud round-trips).