Adaptive Indoor Localization via Missing Access Point Detection

The patent implements dynamic model adaptation by continuously monitoring wireless access point configurations and automatically retraining the machine-learning localization model when changes are detected. This transforms the static model into a dynamic system that adapts to concept drift caused by access point additions or removals, maintaining localization accuracy without requiring complete model retraining.

The system employs feedback mechanisms where the localization performance is continuously evaluated against actual wireless network conditions. When performance degradation is detected due to access point changes, the system triggers automatic model retraining and updates, creating a closed-loop feedback system that maintains reliability despite environmental changes.

Instead of completely retraining the entire localization model every time an access point change is detected, the system applies partial retraining by updating only the specific model components or parameters affected by the change. This selective adaptation approach maintains necessary adaptability while significantly reducing computational overhead and power consumption compared to full model retraining.

The system implements periodic monitoring of access point configurations and triggers model adaptation only when actual changes are detected, rather than continuously retraining. This event-driven periodic action reduces unnecessary computational operations and power consumption while maintaining adaptability to real network changes.